multimodality image registration by maximization of mutual information a new approach to the problem of multimodality medical image registration is proposed using a basic concept from information theory mutual information mi or relative entropy as a new matching criterion the method presented in this paper applies mi to measure the statistical dependence or information redundancy between the image intensities of corresponding voxels in both images which is assumed to be maximal if the images are geometrically aligned maximization of mi is a very general and powerful criterion because no assumptions are made regarding the nature of this dependence and no limiting constraints are imposed on the image content of the modalities involved the accuracy of the mi criterion is validated for rigid body registration of computed tomography ct magnetic resonance mr and photon emission tomography pet images by comparison with the stereotactic registration solution while robustness is evaluated with respect to implementation issues such as interpolation and optimization and image content including partial overlap and image degradation our results demonstrate that subvoxel accuracy with respect to the stereotactic reference solution can be achieved completely automatically and without any prior segmentation feature extraction or other preprocessing steps which makes this method very well suited for clinical applications

latent constrained correlation filter correlation filters are special classifiers designed for shift invariant object recognition which are robust to pattern distortions the recent literature shows that combining a set of sub filters trained based on a single or a small group of images obtains the best performance the idea is equivalent to estimating variable distribution based on the data sampling bagging which can be interpreted as finding solutions variable distribution approximation directly from sampled data space however this methodology fails to account for the variations existed in the data in this paper we introduce an intermediate step solution sampling after the data sampling step to form a subspace in which an optimal solution can be estimated more specifically we propose a new method named latent constrained correlation filters lccf by mapping the correlation filters to a given latent subspace and develop a new learning framework in the latent subspace that embeds distribution related constraints into the original problem to solve the optimization problem we introduce a subspace based alternating direction method of multipliers which is proven to converge at the saddle point our approach is successfully applied to three different tasks including eye localization car detection and object tracking extensive experiments demonstrate that lccf outperforms the state of the art methods 1 1 the source code will be publicly available https github com bczhangbczhang

manifold constraint transfer for visual structure driven optimization abstract in this paper we leverage the manifold structure of visual data in order to improve performance in general optimization problems subject to linear constraints as the main theoretical result we show that manifold constraints can be transferred from the data to the optimized variables if these are linearly correlated we also show that the resulting optimization problem can be solved with an efficient alternating direction method of multipliers that can consistently integrate the manifold constraints during the optimization process this leads to a simplification of the approach which instead of directly optimizing on the manifold we can iteratively recast the problem as the projection over the manifold via an embedding method the proposed method is extremely versatile since it can be applied to different problems including kernel ridge regression krr and sparse coding which have numerous applications in machine learning and computer vision in particular we apply the methods to different problems such as tracking object recognition and categorization showing a consistent increase of performance with respect to the state of the art

aggregating local image descriptors into compact codes this paper addresses the problem of large scale image search three constraints have to be taken into account search accuracy efficiency and memory usage we first present and evaluate different ways of aggregating local image descriptors into a vector and show that the fisher kernel achieves better performance than the reference bag of visual words approach for any given vector dimension we then jointly optimize dimensionality reduction and indexing in order to obtain a precise vector comparison as well as a compact representation the evaluation shows that the image representation can be reduced to a few dozen bytes while preserving high accuracy searching a 100 million image data set takes about 250 ms on one processor core

incremental support vector learning for ordinal regression support vector ordinal regression svor is a popular method to tackle ordinal regression problems however until now there were no effective algorithms proposed to address incremental svor learning due to the complicated formulations of svor recently an interesting accurate on line algorithm was proposed for training nu support vector classification nu svc which can handle a quadratic formulation with a pair of equality constraints in this paper we first present a modified svor formulation based on a sum of margins strategy the formulation has multiple constraints and each constraint includes a mixture of an equality and an inequality then we extend the accurate on line nu svc algorithm to the modified formulation and propose an effective incremental svor algorithm the algorithm can handle a quadratic formulation with multiple constraints where each constraint is constituted of an equality and an inequality more importantly it tackles the conflicts between the equality and inequality constraints we also provide the finite convergence analysis for the algorithm numerical experiments on the several benchmark and real world data sets show that the incremental algorithm can converge to the optimal solution in a finite number of steps and is faster than the existing batch and incremental svor algorithms meanwhile the modified formulation has better accuracy than the existing incremental svor algorithm and is as accurate as the sum of margins based formulation of shashua and levin

sub gaussian estimates of heat kernels on infinite graphs we prove that a two sided sub gaussian estimate of the heat kernel on an infinite weighted graph takes place if and only if the volume growth of the graph is uniformly polynomial and the green kernel admits a uniform polynomial decay

generalized pooling for robust object tracking feature pooling in a majority of sparse coding based tracking algorithms computes final feature vectors only by low order statistics or extreme responses of sparse codes the high order statistics and the correlations between responses to different dictionary items are neglected we present a more generalized feature pooling method for visual tracking by utilizing the probabilistic function to model the statistical distribution of sparse codes since immediate matching between two distributions usually requires high computational costs we introduce the fisher vector to derive a more compact and discriminative representation for sparse codes of the visual target we encode target patches by local coordinate coding utilize gaussian mixture model to compute fisher vectors and finally train semi supervised linear kernel classifiers for visual tracking in order to handle the drifting problem during the tracking process these classifiers are updated online with current tracking results the experimental results on two challenging tracking benchmarks demonstrate that the proposed approach achieves a better performance than the state of the art tracking algorithms

a method for registration of 3 d shapes the authors describe a general purpose representation independent method for the accurate and computationally efficient registration of 3 d shapes including free form curves and surfaces the method handles the full six degrees of freedom and is based on the iterative closest point icp algorithm which requires only a procedure to find the closest point on a geometric entity to a given point the icp algorithm always converges monotonically to the nearest local minimum of a mean square distance metric and the rate of convergence is rapid during the first few iterations therefore given an adequate set of initial rotations and translations for a particular class of objects with a certain level of shape complexity one can globally minimize the mean square distance metric over all six degrees of freedom by testing each initial registration one important application of this method is to register sensed data from unfixtured rigid objects with an ideal geometric model prior to shape inspection experimental results show the capabilities of the registration algorithm on point sets curves and surfaces

multi class boosting with asymmetric binary weak learners we introduce a multi class generalization of adaboost with binary weak learners we use a vectorial codification to represent class labels and a multi class exponential loss function to evaluate classifier responses this representation produces a set of margin values that provide a range of punishments for failures and rewards for successes moreover the stage wise optimization of this model introduces an asymmetric boosting procedure whose costs depend on the number of classes separated by each weak learner in this way the boosting algorithm takes into account class imbalances when building the ensemble the experiments performed compare this new approach favorably to adaboost mh gentleboost and the samme algorithms

adaptive compressive tracking based on locality sensitive histograms abstract recently compressive tracking ct method one of tracking by detection methods has been widely explored in video target tracking because of its high efficiency however it cannot well deal with illumination variations due to its limited target representation to remedy this we propose an adaptive ct algorithm and it significantly improves conventional ct in four aspects first the efficient illumination invariant features extracted on the basis of the locality sensitive histograms are used to represent the appearance of a target which is robust to illumination changes second the color attributes tracker is adopted to predict the target position for re building the new weighted discriminant function which brings the color information to make up for the inadequacy of haar like characteristics third a new model updating mechanism is proposed to preserve the stable features while avoiding the noisy appearance variations during tracking fourth a trajectory rectification method is employed to refine the tracking location when possible inaccurate tracking occurs finally experimental results conducted on benchmark dataset show that our tracker achieves state of the art performance in a comprehensive evaluation

action recognition using 3d histograms of texture and a multi class boosting classifier human action recognition is an important yet challenging task this paper presents a low cost descriptor called 3d histograms of texture 3dhots to extract discriminant features from a sequence of depth maps 3dhots are derived from projecting depth frames onto three orthogonal cartesian planes i e the frontal side and top planes and thus compactly characterize the salient information of a specific action on which texture features are calculated to represent the action besides this fast feature descriptor a new multi class boosting classifier mbc is also proposed to efficiently exploit different kinds of features in a unified framework for action classification compared with the existing boosting frameworks we add a new multi class constraint into the objective function which helps to maintain a better margin distribution by maximizing the mean of margin whereas still minimizing the variance of margin experiments on the msraction3d msrgesture3d msractivity3d and utd mhad data sets demonstrate that the proposed system combining 3dhots and mbc is superior to the state of the art

gracker a graph based planar object tracker matching based algorithms have been commonly used in planar object tracking they often model a planar object as a set of keypoints and then find correspondences between keypoint sets via descriptor matching in previous work unary constraints on appearances or locations are usually used to guide the matching however these approaches rarely utilize structure information of the object and are thus suffering from various perturbation factors in this paper we proposed a graph based tracker named gracker which is able to fully explore the structure information of the object to enhance tracking performance we model a planar object as a graph instead of a simple collection of keypoints to represent its structure then we reformulate tracking as a sequential graph matching process which establishes keypoint correspondence in a geometric graph matching manner for evaluation we compare the proposed gracker with state of the art planar object trackers on three benchmark datasets two public ones and a newly collected one experimental results show that gracker achieves robust tracking results against various environmental variations and outperforms other algorithms in general on the datasets

convex and semi nonnegative matrix factorizations we present several new variations on the theme of nonnegative matrix factorization nmf considering factorizations of the form x fgt we focus on algorithms in which g is restricted to containing nonnegative entries but allowing the data matrix x to have mixed signs thus extending the applicable range of nmf methods we also consider algorithms in which the basis vectors of f are constrained to be convex combinations of the data points this is used for a kernel extension of nmf we provide algorithms for computing these new factorizations and we provide supporting theoretical analysis we also analyze the relationships between our algorithms and clustering algorithms and consider the implications for sparseness of solutions finally we present experimental results that explore the properties of these new methods

visual object tracking via enhanced structural correlation filter in this study we aim to build a robust correlation based visual object tracking system the function of traditional correlation filters for visual tracking is to search the most likely position of target by circularly shifting the search image patch however the search image patch needs to be large enough to cover both the object and background which results in the algorithm being sensitive to changes in background to alleviate this problem we first propose an efficient object surrounding histogram model to suppress the background in this model we build a bayes classifier based on the initial given object and we then apply it to each pixel in subsequent frames with this model the original image can be enhanced in order to eliminate the impact of circular shifting moreover we develop a structural correlation filter that consists of both holistic and local object parts the multiple object parts are adaptively weighted and further aggregated to predict the relative motion from the last frame we conduct extensive experiments on frequently used benchmarks with 51 video sequences the experimental results show that the proposed algorithm achieves outstanding performance especially in terms of heavy occlusion and severe deformation

incremental tensor subspace learning and its applications to foreground segmentation and tracking appearance modeling is very important for background modeling and object tracking subspace learning based algorithms have been used to model the appearances of objects or scenes current vector subspace based algorithms cannot effectively represent spatial correlations between pixel values current tensor subspace based algorithms construct an offline representation of image ensembles and current online tensor subspace learning algorithms cannot be applied to background modeling and object tracking in this paper we propose an online tensor subspace learning algorithm which models appearance changes by incrementally learning a tensor subspace representation through adaptively updating the sample mean and an eigenbasis for each unfolding matrix of the tensor the proposed incremental tensor subspace learning algorithm is applied to foreground segmentation and object tracking for grayscale and color image sequences the new background models capture the intrinsic spatiotemporal characteristics of scenes the new tracking algorithm captures the appearance characteristics of an object during tracking and uses a particle filter to estimate the optimal object state experimental evaluations against state of the art algorithms demonstrate the promise and effectiveness of the proposed incremental tensor subspace learning algorithm and its applications to foreground segmentation and object tracking

rejecting mismatches by correspondence function a novel method icf identifying point correspondences by correspondence function is proposed for rejecting mismatches from given putative point correspondences by analyzing the connotation of homography we introduce a novel concept of correspondence function for two images of a general 3d scene which captures the relationships between corresponding points by mapping a point in one image to its corresponding point in another since the correspondence functions are unknown in real applications we also study how to estimate them from given putative correspondences and propose an algorithm iecf iteratively estimate correspondence function based on diagnostic technique and svm then the proposed icf method is able to reject the mismatches by checking whether they are consistent with the estimated correspondence functions extensive experiments on real images demonstrate the excellent performance of our proposed method in addition the icf is a general method for rejecting mismatches and it is applicable to images of rigid objects or images of non rigid objects with unknown deformation

exploiting superpixel and hybrid hash for kernel based visual tracking superpixel analysis approach is employed to reconstruct target appearance in visual tracking representative superpixel blocks combined with kernel based filter are exploited for roughly target location superpixel block modification method based on hybrid hash is proposed to accurately locate target conventional kernel based filter is improved with peak response scale estimation and color features comprehensive experiments on publicly available datasets demonstrate our tracker superior to state of the art trackers in recent years significant advances in visual tracking have been made and numerous outstanding algorithms have been proposed however the constraint between tracking accuracy and speed has not yet been comprehensively addressed in this paper to address the challenging aspects of visual tracking and in particular to achieve accurate real time tracking we propose a novel real time kernel based visual tracking algorithm based on superpixel clustering and hybrid hash analysis by adopting superpixel clustering and segmentation we reconstruct the appearance model of the target and its surrounding context in the initialization step via introducing the approach of overlap and intensity analysis we divide the reconstructed model into several superpixel blocks based on the theory of circulant matrices and fourier analysis we build a gaussian kernel correlation filter to roughly locate the position of each candidate block to further improve the kernel correlation filter method we compute each block s maximal response value in the confidence map and estimate each block s scale variation based on a peak value comparison additionally we also propose a hybrid hash analysis strategy and integrate it with superpixel analysis for target blocks modification by calculating a hybrid hash sequence based on l a b color and the discrete cosine transform we conduct superpixel block modification to accurately locate the target and estimate the target s scale variation extensive experiments on visual tracking benchmark datasets show that our tracking algorithm outperforms the state of the art algorithms and demonstrate its effectiveness and efficiency

visual tracking via boolean map representations abstract in this paper we present a simple yet effective boolean map based representation that exploits connectivity cues for visual tracking we describe a target object with histogram of oriented gradients and raw color features of which each one is characterized by a set of boolean maps generated by uniformly thresholding their values the boolean maps effectively encode multi scale connectivity cues of the target with different granularities the fine grained boolean maps capture spatially structural details that are effective for precise target localization while the coarse grained ones encode global shape information that are robust to large target appearance variations finally all the boolean maps form together a robust representation that can be approximated by an explicit feature map of the intersection kernel which is fed into a logistic regression classifier with online update and the target location is estimated within a particle filter framework the proposed representation scheme is computationally efficient and facilitates achieving favorable performance in terms of accuracy and robustness against the state of the art tracking methods on the otb50 and vot2016 benchmark datasets

an uncertainty principle for real signals in the fractional fourier transform domain the fractional fourier transform frft can be thought of as a generalization of the fourier transform to rotate a signal representation by an arbitrary angle spl alpha in the time frequency plane a lower bound on the uncertainty product of signal representations in two frft domains for real signals is obtained and it is shown that a gaussian signal achieves the lower bound the effect of shifting and scaling the signal on the uncertainty relation is discussed an example is given in which the uncertainty relation for a real signal is obtained and it is shown that this relation matches with that given by the uncertainty relation derived

extended compressed tracking via random projection based on msers and online ls svm learning the compressed tracking algorithm ct tracker is a well known visual tracking method that models a target object s appearance through sparse random projection however the tracking results are not stable and robust due to the randomness of random projection to solve this problem a more stable and robust approach is proposed for visual tracking based on maximally stable extremal regions msers sparse random projection and online least squares svm classifier ls svm learning to obtain a relatively stable appearance model the stable connected components of an object based on msers in image feature space are extracted with the fusion of msers and sparse random projection we model adaptive object appearance to adapt the variation of appearance additionally an online closed form ls svm is employed to quickly and robustly predict the target object location in a tracking by detection framework experimental results on benchmark sequences show the stability and robustness of the proposed algorithm compared with the existing ct based trackers and other state of the art trackers highlightsa more stable and robust approach is proposed for visual tracking relying on maximally stable extremal regions msers sparse random projection and online least squares svm classifier ls svm learning with the fusion of msers and sparse random projection the stable adaptive object appearance is modeled to adapt the variation of appearance an online closed form ls svm is employed to quickly and robustly predict the target object location

locator checker scaler object tracking using spatially ordered and weighted patch descriptor in this paper we propose a simple yet effective object descriptor and a novel tracking algorithm to track a target object accurately for the object description we divide the bounding box of a target object into multiple patches and describe them with color and gradient histograms then we determine the foreground weight of each patch to alleviate the impacts of background information in the bounding box to this end we perform random walk with restart rwr simulation we then concatenate the weighted patch descriptors to yield the spatially ordered and weighted patch sowp descriptor for the object tracking we incorporate the proposed sowp descriptor into a novel tracking algorithm which has three components locator checker and scaler lcs the locator and the scaler estimate the center location and the size of a target respectively the checker determines whether it is safe to adjust the target scale in a current frame these three components cooperate with one another to achieve robust tracking experimental results demonstrate that the proposed lcs tracker achieves excellent performance on recent benchmarks

bounding multiple gaussians uncertainty with application to object tracking this paper proves the uncertainty bound for the multiple gaussian functions termed multiple gaussians uncertainty mgu which significantly generalizes the uncertainty principle for the single gaussian function first as a theoretical contribution we prove that the momentum velocity and position for the sum of multiple gaussians wave function are theoretically bounded second as for a practical application we show that the bound can be well exploited for object tracking to detect anomalies of local movement in an online learning framework by integrating mgu with a given object tracker we demonstrate that uncertainty principle can provide remarkable robustness in tracking extensive experiments are done to show that the proposed mgu can significantly help base trackers overcome the object drifting and reach state of the art results

robust visual tracking via co trained kernelized correlation filters abstract recent advances in visual tracking have witnessed the importance of discriminative classifiers tasked with distinguishing the target from the background however a single classifier may fail to cope with complex surrounding environment and large appearance variations of the target motivated by multi view learning we equip a basic framework to train a pool of discriminative classifiers jointly in a closed form fashion in this paper it poses an extra regularization term in ridge regression which interacts with other base models in the ensemble through a simple realization of this approach we show co trained kernelized correlation filters cokcf which consist of two kcf trackers are able to outperform the kcf tracker by a larger margin and perform favorably against other state of the art trackers on 63 benchmark video sequences

the dual bootstrap iterative closest point algorithm with application to retinal image registration motivated by the problem of retinal image registration this paper introduces and analyzes a new registration algorithm called dual bootstrap iterative closest point dual bootstrap icp the approach is to start from one or more initial low order estimates that are only accurate in small image regions called bootstrap regions in each bootstrap region the algorithm iteratively 1 refines the transformation estimate using constraints only from within the bootstrap region 2 expands the bootstrap region and 3 tests to see if a higher order transformation model can be used stopping when the region expands to cover the overlap between images steps 1 and 3 the bootstrap steps are governed by the covariance matrix of the estimated transformation estimation refinement step 2 uses a novel robust version of the icp algorithm in registering retinal image pairs dual bootstrap icp is initialized by automatically matching individual vascular landmarks and it aligns images based on detected blood vessel centerlines the resulting quadratic transformations are accurate to less than a pixel on tests involving approximately 6000 image pairs it successfully registered 99 5 of the pairs containing at least one common landmark and 100 of the pairs containing at least one common landmark and at least 35 image overlap

coupling deep correlation filter and online discriminative learning for visual object tracking advances in mathematical models and optimization introduce powerful tools for solving the visual object tracking problem but this fundamental problem remains unsolved due to various challenges such as illumination variation deformation and occlusion recent progress in correlation filter based models has provided an effective and efficient tracking solution this type of trackers exploits the property of convolution theorem to significantly speed up their computational processes however their scheme of training correlation filters restricts them to use only a few negative examples to train their model consequently lowers their performance therefore in this paper we propose to combine the fast calculation advantage of correlation filter with an online discriminative learning model which can fully exploit the negative examples in the context around the target the proposed tracker proceeds in a coarse to fine scheme the proposed tracker will first employ a correlation filter to generate a coarse estimation of the location of the target and then employ a translating model to refine its estimation and calculate the scale variation of the target via a scaling model both the translating model and the scaling model are formulated in the framework of our online discriminative model besides we also propose an effective offline representation learning method to generate robust image feature for our correlation filter extensive experiments on the online object tracking benchmark against the state of the art methods validate the effectiveness of the proposed tracker

efficient additive kernels via explicit feature maps large scale nonlinear support vector machines svms can be approximated by linear ones using a suitable feature map the linear svms are in general much faster to learn and evaluate test than the original nonlinear svms this work introduces explicit feature maps for the additive class of kernels such as the intersection hellinger s and 2 kernels commonly used in computer vision and enables their use in large scale problems in particular we 1 provide explicit feature maps for all additive homogeneous kernels along with closed form expression for all common kernels 2 derive corresponding approximate finite dimensional feature maps based on a spectral analysis and 3 quantify the error of the approximation showing that the error is independent of the data dimension and decays exponentially fast with the approximation order for selected kernels such as 2 we demonstrate that the approximations have indistinguishable performance from the full kernels yet greatly reduce the train test times of svms we also compare with two other approximation methods nystrom s approximation of perronnin et al 1 which is data dependent and the explicit map of maji and berg 2 for the intersection kernel which as in the case of our approximations is data independent the approximations are evaluated on a number of standard data sets including caltech 101 3 daimler chrysler pedestrians 4 and inria pedestrians 5

online similarity learning for visual tracking a novel metric learning approach is proposed to improve the visual tracking performance a computational efficient solution of the proposed learning method is derived the proposed metric learning method can reduce the classification errors incorporating metric learning in visual tracking applications has been demonstrated to be able to improve tracking performance however the optimal metric is mainly derived based on annotated feature vectors by studying their magnitude and intersection angle in complex scenarios the magnitude of feature samples may change drastically confining the matching performance of the distance metric moreover most distance learning methods are optimized in a time consuming iterative way which limits their applications in real time visual tracking to address these problems this paper proposes a novel metric called sphere similarity metric which normalizes the magnitude of feature vectors and measures the distance between pairs of vectors by their intersection angle such metric is robust even when the magnitude of feature vectors changes drastically in complex scenarios we formulate the proposed metric by a convex matrix function which does not require adapting samples magnitude iteratively and can be optimized in a closed form with low computational complexity additionally the proposed similarity metric can be learned in an online manner which accelerates the learning process and improves the tracking accuracy in visual tracking applications experimental results on synthetic data and benchmark video sequences show that the proposed metric learning method achieves better classification accuracy and its application in visual tracking outperforms the state of the art tracking methods

image registration by compression abstract image registration consists in finding the transformation that brings one image into the best possible spatial correspondence with another image in this paper we present a new framework for image registration based on compression the basic idea underlying our approach is the conjecture that two images are correctly registered when we can maximally compress one image given the information in the other the contribution of this paper is twofold first we show that image registration can be formulated as a compression problem second we demonstrate the good performance of the similarity metric introduced by li et al in image registration two different approaches for the computation of this similarity metric are described the kolmogorov version computed using standard real world compressors and the shannon version calculated from an estimation of the entropy rate of the images

consistent multi layer subtask tracker via hyper graph regularization develop the online multi subtask learning framework for robust object tracking with novel task definition the relationships among and inside candidates or training samples are mined by hyper graph regularization simultaneously learn and update the adaptively discriminative subspace and classifier consistent multi subtask tracker is a general model for most existing multi task trackers most multi task learning based trackers adopt similar task definition by assuming that all tasks share a common feature set which cant cover the real situation well in this paper we define the subtasks from the novel perspective and develop a structured and consistent multi layer multi subtask tracker with graph regularization the tracking task is completed by the collaboration of multi layer subtasks different subtasks correspond to the tracking of different parts in the target area the correspondences of the subtasks among the adjacent frames are consistent and smooth the proposed model introduces hyper graph regularizer to preserve the global and local intrinsic geometrical structures among and inside target candidates or trained samples and decomposes the representative matrix of the subtasks into two components low rank property captures the subtask relationship group sparse property identifies the outlier subtasks moreover a collaborate metric scheme is developed to find the best candidate by concerning both discrimination reliability and representation accuracy we show that the proposed multi layer multi subtask learning based tracker is a general model which accommodates most existing multi task trackers with the respective merits encouraging experimental results on a large set of public video sequences justify the effectiveness and robustness of the proposed tracker and achieve comparable performance against many state of the art methods

robust quantization for general similarity search the recent years have witnessed the emerging of vector quantization vq techniques for efficient similarity search vq partitions the feature space into a set of codewords and encodes data points as integer indices using the codewords then the distance between data points can be efficiently approximated by simple memory lookup operations by the compact quantization the storage cost and searching complexity are significantly reduced thereby facilitating efficient large scale similarity search however the performance of several celebrated vq approaches degrades significantly when dealing with noisy data in addition it can barely facilitate a wide range of applications as the distortion measurement only limits to ell 2 norm to address the shortcomings of the squared euclidean ell 2 2 norm loss function employed by the vq approaches in this paper we propose a novel robust and general vq framework named rgvq to enhance both robustness and generalization of vq approaches specifically a ell p q norm loss function is proposed to conduct the ell p norm similarity search rather than the ell 2 norm search and the q th order loss is used to enhance the robustness despite the fact that changing the loss function to ell p q norm makes vq approaches more robust and generic it brings us a challenge that a non smooth and non convex orthogonality constrained ell p q norm function has to be minimized to solve this problem we propose a novel and efficient optimization scheme and specify it to vq approaches and theoretically prove its convergence extensive experiments on benchmark data sets demonstrate that the proposed rgvq is better than the original vq for several approaches especially when searching similarity in noisy data

manifold regularized correlation object tracking in this paper we propose a manifold regularized correlation tracking method with augmented samples to make better use of the unlabeled data and the manifold structure of the sample space a manifold regularization based correlation filter is introduced which aims to assign similar labels to neighbor samples meanwhile the regression model is learned by exploiting the block circulant structure of matrices resulting from the augmented translated samples over multiple base samples cropped from both target and nontarget regions thus the final classifier in our method is trained with positive negative and unlabeled base samples which is a semisupervised learning framework a block optimization strategy is further introduced to learn a manifold regularization based correlation filter for efficient online tracking experiments on two public tracking data sets demonstrate the superior performance of our tracker compared with the state of the art tracking approaches

robust point matching via vector field consensus in this paper we propose an efficient algorithm called vector field consensus for establishing robust point correspondences between two sets of points our algorithm starts by creating a set of putative correspondences which can contain a very large number of false correspondences or outliers in addition to a limited number of true correspondences inliers next we solve for correspondence by interpolating a vector field between the two point sets which involves estimating a consensus of inlier points whose matching follows a nonparametric geometrical constraint we formulate this a maximum a posteriori map estimation of a bayesian model with hidden latent variables indicating whether matches in the putative set are outliers or inliers we impose nonparametric geometrical constraints on the correspondence as a prior distribution using tikhonov regularizers in a reproducing kernel hilbert space map estimation is performed by the em algorithm which by also estimating the variance of the prior model initialized to a large value is able to obtain good estimates very quickly e g avoiding many of the local minima inherent in this formulation we illustrate this method on data sets in 2d and 3d and demonstrate that it is robust to a very large number of outliers even up to 90 we also show that in the special case where there is an underlying parametric geometrical model e g the epipolar line constraint that we obtain better results than standard alternatives like ransac if a large number of outliers are present this suggests a two stage strategy where we use our nonparametric model to reduce the size of the putative set and then apply a parametric variant of our approach to estimate the geometric parameters our algorithm is computationally efficient and we provide code for others to use it in addition our approach is general and can be applied to other problems such as learning with a badly corrupted training data set

exploring sparseness and self similarity for action recognition we propose that the dynamics of an action in video data forms a sparse self similar manifold in the space time volume which can be fully characterized by a linear rank decomposition inspired by the recurrence plot theory we introduce the concept of joint self similarity volume joint ssv to model this sparse action manifold and hence propose a new optimized rank 1 tensor approximation of the joint ssv to obtain compact low dimensional descriptors that very accurately characterize an action in a video sequence we show that these descriptor vectors make it possible to recognize actions without explicitly aligning the videos in time in order to compensate for speed of execution or differences in video frame rates moreover we show that the proposed method is generic in the sense that it can be applied using different low level features such as silhouettes tracked points histogram of oriented gradients and so forth therefore our method does not necessarily require explicit tracking of features in the space time volume our experimental results on five public data sets demonstrate that our method produces promising results and outperforms many baseline methods

non rigid visible and infrared face registration via regularized gaussian fields criterion registration of multi sensor data particularly visible color sensors and infrared sensors is a prerequisite for multimodal image analysis such as image fusion typically the relationships between image pairs are modeled by rigid or affine transformations however this cannot produce accurate alignments when the scenes are not planar for example face images in this paper we propose a regularized gaussian fields criterion for non rigid registration of visible and infrared face images the key idea is to represent an image by its edge map and align the edge maps by a robust criterion with a non rigid model we model the transformation between images in a reproducing kernel hilbert space and a sparse approximation is applied to the transformation to avoid high computational complexity moreover a coarse to fine strategy by applying deterministic annealing is used to overcome local convergence problems the qualitative and quantitative comparisons on two publicly available databases demonstrate that our method significantly outperforms the state of the art method with an affine model as a result our method will be beneficial for fusion based face recognition highlightswe analyze the robustness of gaussian fields criterion both in theory and experiment the gaussian fields criterion is generalized from rigid to the non rigid case we propose a new non rigid registration method to deal with more real world problems a sparse approximation on the transformation is applied to speed up the method we customize and apply the proposed method to visible thermal ir face registration

learning to hash with optimized anchor embedding for scalable retrieval sparse representation and image hashing are powerful tools for data representation and image retrieval respectively the combinations of these two tools for scalable image retrieval i e sparse hashing sh methods have been proposed in recent years and the preliminary results are promising the core of those methods is a scheme that can efficiently embed the high dimensional image features into a low dimensional hamming space while preserving the similarity between features existing sh methods mostly focus on finding better sparse representations of images in the hash space we argue that the anchor set utilized in sparse representation is also crucial which was unfortunately underestimated by the prior art to this end we propose a novel sh method that optimizes the integration of the anchors such that the features can be better embedded and binarized termed as sparse hashing with optimized anchor embedding the central idea is to push the anchors far from the axis while preserving their relative positions so as to generate similar hashcodes for neighboring features we formulate this idea as an orthogonality constrained maximization problem and an efficient and novel optimization framework is systematically exploited extensive experiments on five benchmark image data sets demonstrate that our method outperforms several state of the art related methods

neighborhood rough filter and intuitionistic entropy in unsupervised tracking this paper aims at developing a novel methodology for unsupervised video tracking by exploring the merits of neighborhood rough sets a neighborhood rough filter is designed in this process for initial labeling of continuous moving object s even in the presence of several variations in different feature spaces the locations and color models of the object s are estimated using their lower upper approximations in spatio color neighborhood granular space velocity neighborhood granules and acceleration neighborhood granules are then defined over this estimation to predict the object location in the next frame and to speed up the tracking process a novel concept namely intuitionsistic entropy is introduced here which consists of two new measures neighborhood rough entropy and neighborhood probabilistic entropy to deal with the ambiguities that arise due to occurrence of overlapping occlusion in a video sequence the unsupervised method of tracking is equally good even when compared with some of the state of the art partially supervised methods while showing superior performance during total occlusion

a local start search algorithm to compute exact hausdorff distance for arbitrary point sets this paper discovers the spatial locality feature of point sets and proposes a novel search algorithm called local start search lss to compute the exact hausdorff distance the lss algorithm can greatly reduce the running time when dealing with large scale of point set in which the spatial continuity and distance continuity are very common lss maintains high performance in both overlap and non overlap situations of a pair of regular point sets while earlybreak experiences degraded performance in overlap situations furthermore for general point sets in overlap situations the preprocess of excluding the intersection in earlybreak will fail however lss can still process the general point sets with different point sizes after ordering the sets our algorithm outperforms the state of the art algorithm experiments demonstrate the efficiency and accuracy of the proposed method the hausdorff distance hd is a very important similarity measurement in pattern recognition shape matching and artificial intelligence because of its inherent computational complexity computing the hd using the naivehd brute force algorithm is difficult especially for comparing the similarity between large scale point sets in the time of big data to overcome this problem we propose a novel efficient and general algorithm for computing the exact hd for arbitrary point sets which takes advantage of the spatial locality of point sets namely local start search lss different from the state of the art algorithm earlybreak in pami 2015 our idea comes from the observation and fact that the neighbor points of a break position in the current loop have higher probability to break the next loop than other points therefore in our algorithm we add a new mechanism to record the current break position as a start position which is initialized as search center of the next loop then lss executes the next loop by scanning the neighbor points around the center in this way lss maintains high performance in both overlap and non overlap situations furthermore the lss algorithm can process arbitrary data by adopting the morton curve to establish the order of scattered point sets while the earlybreak is mainly applied to regular data which require the same grid size such as medical images or voxel data in the non overlapping situation when comparing pairs of arbitrary point sets lss achieves performance as good as earlybreak algorithm while in the overlapping situation when comparing pairs of arbitrary point sets lss is faster than earlybreak by three orders of magnitude thus as a whole lss outperforms earlybreak in addition lss compared against the increment hausdorff distance calculation algorithm inc and significantly outperforms it by an order of magnitude faster experiments demonstrate the efficiency and accuracy of the proposed method

structure regularized compressive tracking with online data driven sampling being a powerful appearance model compressive random projection derives effective haar like features from non rotated 4 d parameterized rectangles thus supporting fast and reliable object tracking in this paper we show that such successful fast compressive tracking scheme can be further significantly improved by structural regularization and online data driven sampling our major contribution is threefold first we find that superpixel guided compressive projection can generate more discriminative features by sufficiently capturing rich local structural information of images second we propose fast directional integration that enables low cost extraction of feasible haar like features from arbitrarily rotated 5 d parameterized rectangles to realize more accurate object localization third beyond naive dense uniform sampling we present two practical online data driven sampling strategies to produce less yet more effective candidate and training samples for object detection and classifier updating respectively extensive experiments on real world benchmark data sets validate the superior performance i e much better object localization ability and robustness of the proposed approach over state of the art trackers

additive logistic regression a statistical view of boosting boosting is one of the most important recent developments in classification methodology boosting works by sequentially applying a classification algorithm to reweighted versions of the training data and then taking a weighted majority vote of the sequence of classifiers thus produced for many classification algorithms this simple strategy results in dramatic improvements in performance we show that this seemingly mysterious phenomenon can be understood in terms of well known statistical principles namely additive modeling and maximum likelihood for the two class problem boosting can be viewed as an approximation to additive modeling on the logistic scale using maximum bernoulli likelihood as a criterion we develop more direct approximations and show that they exhibit nearly identical results to boosting direct multiclass generalizations based on multinomial likelihood are derived that exhibit performance comparable to other recently proposed multiclass generalizations of boosting in most situations and far superior in some we suggest a minor modification to boosting that can reduce computation often by factors of 10 to 50 finally we apply these insights to produce an alternative formulation of boosting decision trees this approach based on best first truncated tree induction often leads to better performance and can provide interpretable descriptions of the aggregate decision rule it is also much faster computationally making it more suitable to large scale data mining applications

riesz transform and l p cohomology for manifolds with euclidean ends let m be a smooth riemannian manifold which is the union of a compact part and a finite number of euclidean ends rr n setminus b 0 r for some r 0 each of which carries the standard metric our main result is that the riesz transform on m is bounded from l p m to l p m t m for 1 n the result is new for 2 2 for a more general class of manifolds assume that m is a n dimensional complete manifold satisfying the nash inequality and with an o r n upper bound on the volume growth of geodesic balls we show that boundedness of the riesz transform on l p for some p 2 implies a hodge de rham interpretation of the l p cohomology in degree 1 and that the map from l 2 to l p cohomology in this degree is injective

a feasible method for optimization with orthogonality constraints minimization with orthogonality constraints e g x top x i and or spherical constraints e g vert x vert 2 1 has wide applications in polynomial optimization combinatorial optimization eigenvalue problems sparse pca p harmonic flows 1 bit compressive sensing matrix rank minimization etc these problems are difficult because the constraints are not only non convex but numerically expensive to preserve during iterations to deal with these difficulties we apply the cayley transform a crank nicolson like update scheme to preserve the constraints and based on it develop curvilinear search algorithms with lower flops compared to those based on projections and geodesics the efficiency of the proposed algorithms is demonstrated on a variety of test problems in particular for the maxcut problem it exactly solves a decomposition formulation for the sdp relaxation for polynomial optimization nearest correlation matrix estimation and extreme eigenvalue problems the proposed algorithms run very fast and return solutions no worse than those from their state of the art algorithms for the quadratic assignment problem a gap 0 842 to the best known solution on the largest problem tai256c in qaplib can be reached in 5 min on a typical laptop

graph regularized nonnegative matrix factorization for data representation matrix factorization techniques have been frequently applied in information retrieval computer vision and pattern recognition among them nonnegative matrix factorization nmf has received considerable attention due to its psychological and physiological interpretation of naturally occurring data whose representation may be parts based in the human brain on the other hand from the geometric perspective the data is usually sampled from a low dimensional manifold embedded in a high dimensional ambient space one then hopes to find a compact representation which uncovers the hidden semantics and simultaneously respects the intrinsic geometric structure in this paper we propose a novel algorithm called graph regularized nonnegative matrix factorization gnmf for this purpose in gnmf an affinity graph is constructed to encode the geometrical information and we seek a matrix factorization which respects the graph structure our empirical study shows encouraging results of the proposed algorithm in comparison to the state of the art algorithms on real world problems

iterative quantization a procrustean approach to learning binary codes for large scale image retrieval this paper addresses the problem of learning similarity preserving binary codes for efficient similarity search in large scale image collections we formulate this problem in terms of finding a rotation of zero centered data so as to minimize the quantization error of mapping this data to the vertices of a zero centered binary hypercube and propose a simple and efficient alternating minimization algorithm to accomplish this task this algorithm dubbed iterative quantization itq has connections to multiclass spectral clustering and to the orthogonal procrustes problem and it can be used both with unsupervised data embeddings such as pca and supervised embeddings such as canonical correlation analysis cca the resulting binary codes significantly outperform several other state of the art methods we also show that further performance improvements can result from transforming the data with a nonlinear kernel mapping prior to pca or cca finally we demonstrate an application of itq to learning binary attributes or classemes on the imagenet data set

regularized vector field learning with sparse approximation for mismatch removal in vector field learning regularized kernel methods such as regularized least squares require the number of basis functions to be equivalent to the training sample size n the learning process thus has o n 3 and o n 2 in the time and space complexity respectively this poses significant burden on the vector learning problem for large datasets in this paper we propose a sparse approximation to a robust vector field learning method sparse vector field consensus sparsevfc and derive a statistical learning bound on the speed of the convergence we apply sparsevfc to the mismatch removal problem the quantitative results on benchmark datasets demonstrate the significant speed advantage of sparsevfc over the original vfc algorithm two orders of magnitude faster without much performance degradation we also demonstrate the large improvement by sparsevfc over traditional methods like ransac moreover the proposed method is general and it can be applied to other applications in vector field learning

characterization of sub gaussian heat kernel estimates on strongly recurrent graphs sub gaussian estimates for random walks are typical of fractal graphs we characterize them in the strongly recurrent case in terms of resistance estimates only without assuming elliptic harnack inequalities

robust object tracking with discrete graph based multiple experts variations of target appearances due to illumination changes heavy occlusions and target deformations are the major factors for tracking drift in this paper we show that the tracking drift can be effectively corrected by exploiting the relationship between the current tracker and its historical tracker snapshots here a multi expert framework is established by the current tracker and its historical trained tracker snapshots the proposed scheme is formulated into a unified discrete graph optimization framework whose nodes are modeled by the hypotheses of the multiple experts furthermore an exact solution of the discrete graph exists giving the object state estimation at each time step with the unary and binary compatibility graph scores defined properly the proposed framework corrects the tracker drift via selecting the best expert hypothesis which implicitly analyzes the recent performance of the multi expert by only evaluating graph scores at the current frame three base trackers are integrated into the proposed framework to validate its effectiveness we first integrate the online svm on a budget algorithm into the framework with significant improvement then the regression correlation filters with hand crafted features and deep convolutional neural network features are introduced respectively to further boost the tracking performance the proposed three trackers are extensively evaluated on three data sets tb 50 tb 100 and vot2015 the experimental results demonstrate the excellent performance of the proposed approaches against the state of the art methods

fast pixelwise adaptive visual tracking of non rigid objects in this paper we present a new algorithm for real time single object tracking in videos in unconstrained environments the algorithm comprises two different components that are trained in one shot at the first video frame a detector that makes use of the generalized hough transform with color and gradient descriptors and a probabilistic segmentation method based on global models for foreground and background color distributions both components work at pixel level and are used for tracking in a combined way adapting each other in a co training manner moreover we propose an adaptive shape model as well as a new probabilistic method for updating the scale of the tracker through effective model adaptation and segmentation the algorithm is able to track objects that undergo rigid and non rigid deformations and considerable shape and appearance variations the proposed tracking method has been thoroughly evaluated on challenging benchmarks and outperforms the state of the art tracking methods designed for the same task finally a very efficient implementation of the proposed models allows for extremely fast tracking

shape matching and object recognition using shape contexts we present a novel approach to measuring similarity between shapes and exploit it for object recognition in our framework the measurement of similarity is preceded by 1 solving for correspondences between points on the two shapes 2 using the correspondences to estimate an aligning transform in order to solve the correspondence problem we attach a descriptor the shape context to each point the shape context at a reference point captures the distribution of the remaining points relative to it thus offering a globally discriminative characterization corresponding points on two similar shapes will have similar shape contexts enabling us to solve for correspondences as an optimal assignment problem given the point correspondences we estimate the transformation that best aligns the two shapes regularized thin plate splines provide a flexible class of transformation maps for this purpose the dissimilarity between the two shapes is computed as a sum of matching errors between corresponding points together with a term measuring the magnitude of the aligning transform we treat recognition in a nearest neighbor classification framework as the problem of finding the stored prototype shape that is maximally similar to that in the image results are presented for silhouettes trademarks handwritten digits and the coil data set

modeling the shape of the scene a holistic representation of the spatial envelope in this paper we propose a computational model of the recognition of real world scenes that bypasses the segmentation and the processing of individual objects or regions the procedure is based on a very low dimensional representation of the scene that we term the spatial envelope we propose a set of perceptual dimensions naturalness openness roughness expansion ruggedness that represent the dominant spatial structure of a scene then we show that these dimensions may be reliably estimated using spectral and coarsely localized information the model generates a multidimensional space in which scenes sharing membership in semantic categories e g streets highways coasts are projected closed together the performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category

robust tracking via locally structured representation representation method is critical to visual tracking a robust representation describes the target accurately leading to good tracking performance in this work a novel representation is proposed which is designed to be simultaneously low rank and joint sparse for the local patches within a target region in this representation the subspace structure is exploited by the low rank constraint to reflect the global information of all the patches and the sparsity structure is captured by the joint sparsity restriction to describe the locally intimate relationship between the neighboring patches importantly to make the representation computationally applicable to visual tracking a novel fast algorithm based on greedy strategy is proposed and the performance analysis of this algorithm is also presented thus the tracking in this work is formulated as a locally low rank and joint sparse matching problem within particle filtering framework a large number of experimental results show that the tracking drift problem is effectively alleviated in various challenging situations by using the proposed representation method both qualitative and quantitative evaluations demonstrate that the proposed tracker performs favorably against many other state of the art trackers benefitting from the good adaptive capability of the representation all the parameters of the proposed tracking algorithm are fixed in all the experiments

gaussian fields a new criterion for 3d rigid registration abstract this paper introduces a new and simple criterion for rigid registration based on gaussian fields the criterion is always differentiable and convex in a large neighborhood of the alignment parameters allowing for the use of well proven optimization techniques using this method we can extend the size of the region of convergence so that no close initialization is needed thus overcoming local convergence problems of iterative closest point algorithms furthermore the gaussian energy function can be evaluated with linear complexity using the fast gauss transform which permits efficient implementation of the registration algorithm experimental analysis on real world data sets shows the usefulness and points the limits of the approach

gaussian field consensus a robust nonparametric matching method for outlier rejection abstract in this paper we propose a robust method called gaussian field consensus gfc for outlier rejection from given putative point set matching correspondences finding correct correspondences inliers is a key component in many computer vision and pattern recognition tasks and the goal of outlier mismatch rejection is to fit the transformation function that maps one feature point set to another our gfc starts by inputting a putative correspondence set which is contaminated by many outliers and the main task of our gfc is to identify the underlying true correspondences from outliers then we formulate this challenging problem by gaussian field nonparametric matching model which bases on the exponential distance loss and kernel method in a reproducing kernel hilbert space next we introduce a local linear constraint based on the regularization theory to preserve the topological structure of the feature points moreover the sparse approximation is used to reduce the search space in this way we can handle a large number of points easily finally we test the gfc method on several real image datasets in the presence of outliers where the experimental results show that our proposed method outperforms current state of the art methods in most test scenarios

a sparse embedding and least variance encoding approach to hashing hashing is becoming increasingly important in large scale image retrieval for fast approximate similarity search and efficient data storage many popular hashing methods aim to preserve the knn graph of high dimensional data points in the low dimensional manifold space which is however difficult to achieve when the number of samples is big in this paper we propose an effective and efficient hashing approach by sparsely embedding a sample in the training sample space and encoding the sparse embedding vector over a learned dictionary to this end we partition the sample space into clusters via a linear spectral clustering method and then represent each sample as a sparse vector of normalized probabilities that it falls into its several closest clusters this actually embeds each sample sparsely in the sample space the sparse embedding vector is employed as the feature of each sample for hashing we then propose a least variance encoding model which learns a dictionary to encode the sparse embedding feature and consequently binarize the coding coefficients as the hash codes the dictionary and the binarization threshold are jointly optimized in our model experimental results on benchmark data sets demonstrated the effectiveness of the proposed approach in comparison with state of the art methods

robust non rigid point set registration using spatially constrained gaussian fields estimating transformations from degraded point sets is necessary for many computer vision and pattern recognition applications in this paper we propose a robust non rigid point set registration method based on spatially constrained context aware gaussian fields we first construct a context aware representation e g shape context for assignment initialization then we use a graph laplacian regularized gaussian fields to estimate the underlying transformation from the likely correspondences on the one hand the intrinsic manifold is considered and used to preserve the geometrical structure and a priori knowledge of the point set is extracted on the other hand by using the deterministic annealing the presented method is extended to a projected high dimensional feature space i e reproducing kernel hilbert space through a kernel trick to solve the transformation in which the local structure is propagated by the coarse to fine scaling strategy in this way the proposed method gradually recovers much more correct correspondences and then estimates the transformation parameters accurately and robustly when facing degradations experimental results on 2d and 3d synthetic and real data point sets demonstrate that the proposed method reaches better performance than the state of the art algorithms

point set registration coherent point drift point set registration is a key component in many computer vision tasks the goal of point set registration is to assign correspondences between two sets of points and to recover the transformation that maps one point set to the other multiple factors including an unknown nonrigid spatial transformation large dimensionality of point set noise and outliers make the point set registration a challenging problem we introduce a probabilistic method called the coherent point drift cpd algorithm for both rigid and nonrigid point set registration we consider the alignment of two point sets as a probability density estimation problem we fit the gaussian mixture model gmm centroids representing the first point set to the data the second point set by maximizing the likelihood we force the gmm centroids to move coherently as a group to preserve the topological structure of the point sets in the rigid case we impose the coherence constraint by reparameterization of gmm centroid locations with rigid parameters and derive a closed form solution of the maximization step of the em algorithm in arbitrary dimensions in the nonrigid case we impose the coherence constraint by regularizing the displacement field and using the variational calculus to derive the optimal transformation we also introduce a fast algorithm that reduces the method computation complexity to linear we test the cpd algorithm for both rigid and nonrigid transformations in the presence of noise outliers and missing points where cpd shows accurate results and outperforms current state of the art methods

a computational approach to edge detection this paper describes a computational approach to edge detection the success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points these goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution we define detection and localization criteria for a class of edges and present mathematical forms for these criteria as functionals on the operator impulse response a third criterion is then added to ensure that the detector has only one response to a single edge we use the criteria in numerical optimization to derive detectors for several common image features including step edges on specializing the analysis to step edges we find that there is a natural uncertainty principle between detection and localization performance which are the two main goals with this principle we derive a single operator shape which is optimal at any scale the optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a gaussian smoothed image we extend this simple detector using operators of several widths to cope with different signal to noise ratios in the image we present a general method called feature synthesis for the fine to coarse integration of information from operators at different scales finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge

laplacian eigenmaps for dimensionality reduction and data representation one of the central problems in machine learning and pattern recognition is to develop appropriate representations for complex data we consider the problem of constructing a representation for data lying on a low dimensional manifold embedded in a high dimensional space drawing on the correspondence between the graph laplacian the laplace beltrami operator on the manifold and the connections to the heat equation we propose a geometrically motivated algorithm for representing the high dimensional data the algorithm provides a computationally efficient approach to nonlinear dimensionality reduction that has locality preserving properties and a natural connection to clustering some potential applications and illustrative examples are discussed

distortion aware correlation tracking recently correlation filter cf based tracking methods have attracted considerable attention because of their high speed performance however distortion which refers to the phenomenon that the correlation outputs of cf based trackers are distorted remains a major obstacle for these methods in this paper we propose a distortion aware correlation filter framework which can detect distortions and recover from tracking failures our framework employs a simple yet effective feature termed normed correlation response to detect distortions meanwhile we introduce a competition mechanism to handle distortions in which we build a specialized graph to formulate and handle tracking under distortion as a maximum multi clique problem furthermore a global local context model is exploited to alleviate underlying distortions during the tracking process extensive experiments on the online tracking benchmark show that our tracker can find the optimal target trajectory during the distortion period and retrieve the possibly missing target consequently outperforms the state of the art methods and improves the performance of cf based trackers favorably

the uncertainty principle in image processing the uncertainty principle is recognized as one of the fundamental results in signal processing its role in inference is however less well known outside of quantum mechanics it is the aim of this paper to provide a unified approach to the problem of uncertainty in image processing it is shown that uncertainty can be derived from the fundamental constraints on the process of vision the requirements for class defining operations which are both shift invariant and insensitive to changes in illumination it is thus shown that uncertainty plays a key role in the language of vision since it affects the choice of both the alphabet the elementary signals and the syntax the inferential structure of vision the report is concluded with a number of practical illustrations of these ideas taken from such image processing tasks as enhancement data compression and segmentation

robust point set registration using gaussian mixture models in this paper we present a unified framework for the rigid and nonrigid point set registration problem in the presence of significant amounts of noise and outliers the key idea of this registration framework is to represent the input point sets using gaussian mixture models then the problem of point set registration is reformulated as the problem of aligning two gaussian mixtures such that a statistical discrepancy measure between the two corresponding mixtures is minimized we show that the popular iterative closest point icp method and several existing point set registration methods in the field are closely related and can be reinterpreted meaningfully in our general framework our instantiation of this general framework is based on the the l2 distance between two gaussian mixtures which has the closed form expression and in turn leads to a computationally efficient registration algorithm the resulting registration algorithm exhibits inherent statistical robustness has an intuitive interpretation and is simple to implement we also provide theoretical and experimental comparisons with other robust methods for point set registration

long time behaviour of heat flow global estimates and exact asymptotics we obtain global upper and lower bounds on the heat kernel of an elliptic second order differential operator which become sharp in certain long time and large space asymptotics we prove a generalization of aronson s gaussian bounds which identifies correctly an effective drift for heat flow in the case of periodic coefficients we give variational characterizations of the effective conductivity which is then made to appear in heat kernel bounds these results are for heat kernels with measurable coefficients for differentiable coefficients we prove tighter estimates in which the rate of homogenization is known to be optimal

interacting multiview tracker a robust algorithm is proposed for tracking a target object in dynamic conditions including motion blurs illumination changes pose variations and occlusions to cope with these challenging factors multiple trackers based on different feature representations are integrated within a probabilistic framework each view of the proposed multiview multi channel feature learning algorithm is concerned with one particular feature representation of a target object from which a tracker is developed with different levels of reliability with the multiple trackers the proposed algorithm exploits tracker interaction and selection for robust tracking performance in the tracker interaction a transition probability matrix is used to estimate dependencies between trackers multiple trackers communicate with each other by sharing information of sample distributions the tracker selection process determines the most reliable tracker with the highest probability to account for object appearance changes the transition probability matrix and tracker probability are updated in a recursive bayesian framework by reflecting the tracker reliability measured by a robust tracker likelihood function that learns to account for both transient and stable appearance changes experimental results on benchmark datasets demonstrate that the proposed interacting multiview algorithm performs robustly and favorably against state of the art methods in terms of several quantitative metrics

occlusion aware fragment based tracking with spatial temporal consistency in this paper we present a robust tracking method by exploiting a fragment based appearance model with consideration of both temporal continuity and discontinuity information from the perspective of probability theory the proposed tracking algorithm can be viewed as a two stage optimization problem in the first stage by adopting the estimated occlusion state as a prior the optimal state of the tracked object can be obtained by solving an optimization problem where the objective function is designed based on the classification score occlusion prior and temporal continuity information in the second stage we propose a discriminative occlusion model which exploits both foreground and background information to detect the possible occlusion and also models the consistency of occlusion labels among different frames in addition a simple yet effective training strategy is introduced during the model training and updating process with which the effects of spatial temporal consistency are properly weighted the proposed tracker is evaluated by using the recent benchmark data set on which the results demonstrate that our tracker performs favorably against other state of the art tracking algorithms

high speed tracking with kernelized correlation filters the core component of most modern trackers is a discriminative classifier tasked with distinguishing between the target and the surrounding environment to cope with natural image changes this classifier is typically trained with translated and scaled sample patches such sets of samples are riddled with redundancies any overlapping pixels are constrained to be the same based on this simple observation we propose an analytic model for datasets of thousands of translated patches by showing that the resulting data matrix is circulant we can diagonalize it with the discrete fourier transform reducing both storage and computation by several orders of magnitude interestingly for linear regression our formulation is equivalent to a correlation filter used by some of the fastest competitive trackers for kernel regression however we derive a new kernelized correlation filter kcf that unlike other kernel algorithms has the exact same complexity as its linear counterpart building on it we also propose a fast multi channel extension of linear correlation filters via a linear kernel which we call dual correlation filter dcf both kcf and dcf outperform top ranking trackers such as struck or tld on a 50 videos benchmark despite running at hundreds of frames per second and being implemented in a few lines of code algorithm 1 to encourage further developments our tracking framework was made open source

semi supervised tensor based graph embedding learning and its application to visual discriminant tracking an appearance model adaptable to changes in object appearance is critical in visual object tracking in this paper we treat an image patch as a two order tensor which preserves the original image structure we design two graphs for characterizing the intrinsic local geometrical structure of the tensor samples of the object and the background graph embedding is used to reduce the dimensions of the tensors while preserving the structure of the graphs then a discriminant embedding space is constructed we prove two propositions for finding the transformation matrices which are used to map the original tensor samples to the tensor based graph embedding space in order to encode more discriminant information in the embedding space we propose a transfer learning based semi supervised strategy to iteratively adjust the embedding space into which discriminative information obtained from earlier times is transferred we apply the proposed semi supervised tensor based graph embedding learning algorithm to visual tracking the new tracking algorithm captures an object s appearance characteristics during tracking and uses a particle filter to estimate the optimal object state experimental results on the cvpr 2013 benchmark dataset demonstrate the effectiveness of the proposed tracking algorithm

duplex metric learning for image set classification image set classification has attracted much attention because of its broad applications despite the success made so far the problems of intra class diversity and inter class similarity still remain two major challenges to explore a possible solution to these challenges this paper proposes a novel approach termed duplex metric learning dml for image set classification the proposed dml consists of two progressive metric learning stages with different objectives used for feature learning and image classification respectively the metric learning regularization is not only used to learn powerful feature representations but also well explored to train an effective classifier at the first stage we first train a discriminative stacked autoencoder dsae by layer wisely imposing a metric learning regularization term on the neurons in the hidden layers and meanwhile minimizing the reconstruction error to obtain new feature mappings in which similar samples are mapped closely to each other and dissimilar samples are mapped farther apart at the second stage we discriminatively train a classifier and simultaneously fine tune the dsae by optimizing a new objective function which consists of a classification error term and a metric learning regularization term finally two simple voting strategies are devised for image set classification based on the learnt classifier in the experiments we extensively evaluate the proposed framework for the tasks of face recognition object recognition and face verification on several commonly used data sets and state of the art results are achieved in comparison with existing methods

on learning vector valued functions in this letter we provide a study of learning in a hilbert space of vectorvalued functions we motivate the need for extending learning theory of scalar valued functions by practical considerations and establish some basic results for learning vector valued functions that should prove useful in applications specifically we allow an output space y to be a hilbert space and we consider a reproducing kernel hilbert space of functions whose values lie in y in this setting we derive the form of the minimal norm interpolant to a finite set of data and apply it to study some regularization functionals that are important in learning theory we consider specific examples of such functionals corresponding to multiple output regularization networks and support vector machines for both regression and classification finally we provide classes of operator valued kernels of the dot product and translation invariant type

removing mismatches for retinal image registration via multi attribute driven regularized mixture model in order to address the problem of retinal image registration this paper proposes and analyzes a novel and general matching algorithm called multi attribute driven regularized mixture model mad rmm mismatches removal can play a key role in image registration which refers to establish reliable matches between two point sets here the presented approach starts from multi feature attributes which are used to guide the feature matching to identify inliers correct matches from outliers incorrect matches and then estimates the spatial transformation in this paper motivated by the problem of feature matching that the initial correspondence is always contaminated by outliers thereby we formulate this issue as a probability deformable mixture model which consists of gaussian components for inliers and uniform components for outliers moreover the algorithm takes full advantage of using multiple attributes for better general matching performance here we are assuming all inliers are mapped into a high dimensional feature space namely reproducing kernel hilbert space rkhs and the closed form solution to the mapping function is given by the representation theorem with l2 norm regularization under the expectation maximization em algorithm finally we evaluate the performance of the algorithm by applying it to retinal image registration on several datasets where experimental results demonstrate that the mad rmm outperforms current state of the art methods and shows the robustness to outliers on real retinal images

visual tracking via discriminative sparse similarity map in this paper we cast the tracking problem as finding the candidate that scores highest in the evaluation model based upon a matrix called discriminative sparse similarity map dss map this map demonstrates the relationship between all the candidates and the templates and it is constructed based on the solution to an innovative optimization formulation named multitask reverse sparse representation formulation which searches multiple subsets from the whole candidate set to simultaneously reconstruct multiple templates with minimum error a customized apg method is derived for getting the optimum solution in matrix form within several iterations this formulation allows the candidates to be evaluated accurately in parallel rather than one by one like most sparsity based trackers do and meanwhile considers the relationship between candidates therefore it is more superior in terms of cost performance ratio the discriminative information containing in this map comes from a large template set with multiple positive target templates and hundreds of negative templates a laplacian term is introduced to keep the coefficients similarity level in accordance with the candidates similarities thereby making our tracker more robust a pooling approach is proposed to extract the discriminative information in the dss map for easily yet effectively selecting good candidates from bad ones and finally get the optimum tracking results plenty experimental evaluations on challenging image sequences demonstrate that the proposed tracking algorithm performs favorably against the state of the art methods

discrete multi gabor expansions a discrete multi gabor expansion dmge is developed to meet the requirements of localized and refined time frequency tf representation of signals the dmge uses multiple windows and their translations and complex modulations as synthesis or analysis waveforms it includes and generalizes the metaplectic translation modulation and dilation representations which are useful in signal analysis uniform nonuniform and proportional time sampling schemes are analyzed the fundamental features and the importance of the dmge are discussed we focus on the construction of dmge and deriving fast algorithms for the computation of related multi analysis sequences with matrix algebra the algorithms derived apply to both multi gabor expansions and uni window gabor expansions another useful feature of the dmge lies in the fact that the multi gabor transform can be realized in a parallel fft based implementation structure examples of dmge and their applications to tf analysis are also discussed

registration of challenging image pairs initialization estimation and decision our goal is an automated 2d image pair registration algorithm capable of aligning images taken of a wide variety of natural and man made scenes as well as many medical images the algorithm should handle low overlap substantial orientation and scale differences large illumination variations and physical changes in the scene an important component of this is the ability to automatically reject pairs that have no overlap or have too many differences to be aligned well we propose a complete algorithm including techniques for initialization for estimating transformation parameters and for automatically deciding if an estimate is correct keypoints extracted and matched between images are used to generate initial similarity transform estimates each accurate over a small region these initial estimates are rank ordered and tested individually in succession each estimate is refined using the dual bootstrap icp algorithm driven by matching of multiscale features a three part decision criteria combining measurements of alignment accuracy stability in the estimate and consistency in the constraints determines whether the refined transformation estimate is accepted as correct experimental results on a data set of 22 challenging image pairs show that the algorithm effectively aligns 19 of the 22 pairs and rejects 99 8 percent of the misalignments that occur when all possible pairs are tried the algorithm substantially out performs algorithms based on keypoint matching alone

hashing on nonlinear manifolds learning based hashing methods have attracted considerable attention due to their ability to greatly increase the scale at which existing algorithms may operate most of these methods are designed to generate binary codes preserving the euclidean similarity in the original space manifold learning techniques in contrast are better able to model the intrinsic structure embedded in the original high dimensional data the complexities of these models and the problems with out of sample data have previously rendered them unsuitable for application to large scale embedding however in this paper how to learn compact binary embeddings on their intrinsic manifolds is considered in order to address the above mentioned difficulties an efficient inductive solution to the out of sample data problem and a process by which nonparametric manifold learning may be used as the basis of a hashing method are proposed the proposed approach thus allows the development of a range of new hashing techniques exploiting the flexibility of the wide variety of manifold learning approaches available it is particularly shown that hashing on the basis of t distributed stochastic neighbor embedding outperforms state of the art hashing methods on large scale benchmark data sets and is very effective for image classification with very short code lengths it is shown that the proposed framework can be further improved for example by minimizing the quantization error with learned orthogonal rotations without much computation overhead in addition a supervised inductive manifold hashing framework is developed by incorporating the label information which is shown to greatly advance the semantic retrieval performance

sharp explicit lower bounds of heat kernels by using logarithmic transformations an explicit lower bound estimate of heat kernels is obtained for diffusion processes on riemannian manifolds this estimate is sharp for both short and long times especially for heat kernels on a compact manifold and is extended to manifolds with unbounded curvature