label propagation and higher order constraint based segmentation of fluid associated regions in retinal sd oct images the segmentation of the fluid associated region in the retina plays an important role in the treatment of retinal diseases such as diabetic retinopathy dr and age related macular degeneration amd the existing methods for the detection of the fluid region generally suffer from the limitation of the segmentation accuracy and the expensive time costs to overcome these problems in this paper we propose an interactive segmentation method for the fluid associated region in three dimensional 3 d spectral domain optical coherence tomography sd oct retinal imaging in which only a few seeds in one sd oct slice are needed after which the algorithm finishes the segmentation automatically to improve the segmentation accuracy the higher order constraint is introduced into the conventional markov random field mrf framework to impose the superpixel consistency to maintain temporal coherence of the 3 d sd oct slices the labeling information is propagated slice by slice via a proposed motion estimation based algorithm the proposed higher order based energy function can be efficiently solved by the max flow algorithm on a specified graph with several auxiliary nodes experiments on 28 sd oct cubes demonstrate the competitiveness of the proposed method compared with the state of the art methods

semi supervised lpp algorithms for learning to rank based visual search reranking two feature dimensionality reduction methods for search reranking are proposed the sslpp algorithm improves lpp by incorporating the relevance degree information the proposed pcc sslpp algorithm models the correlation information into sslpp the proposed methods achieve better ndcg performance recent years have witnessed the success of learning to rank technique in visual search reranking generally dimensionality reduction is one of the important stages in this kind of method to deal with the high dimensional visual features however these methods are typically designed for classification and retrieval applications rather than ranking direct utilization of them cannot guarantee the best performance in ranking applications as ranking information e g relevance degree label is absent to solve this problem a semi supervised lpp algorithm called sslpp is proposed the method incorporates the relevance degree information into a manifold dimensionality reduction method i e locality preserving projections lpp to accurately preserve the correlation information between two images in the low intrinsic dimensional space a pearson correlation coefficient pcc based sslpp algorithm called pcc sslpp is further developed the pcc sslpp method models the correlation information between two images in the graph construction step of sslpp besides a novel visual search reranking scheme is also presented based on the proposed sslpp pcc sslpp and learning to rank experiments on two popular real world image reranking datasets demonstrate the effectiveness of the proposed scheme and algorithms in visual search reranking

eigenfaces vs fisherfaces recognition using class specific linear projection we develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression taking a pattern classification approach we consider each pixel in an image as a coordinate in a high dimensional space we take advantage of the observation that the images of a particular face under varying illumination but fixed pose lie in a 3d linear subspace of the high dimensional image space if the face is a lambertian surface without shadowing however since faces are not truly lambertian surfaces and do indeed produce self shadowing images will deviate from this linear subspace rather than explicitly modeling this deviation we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation our projection method is based on fisher s linear discriminant and produces well separated classes in a low dimensional subspace even under severe variation in lighting and facial expressions the eigenface technique another method based on linearly projecting the image space to a low dimensional subspace has similar computational requirements yet extensive experimental results demonstrate that the proposed fisherface method has error rates that are lower than those of the eigenface technique for tests on the harvard and yale face databases

probabilistic visual learning for object representation we present an unsupervised technique for visual learning which is based on density estimation in high dimensional spaces using an eigenspace decomposition two types of density estimates are derived for modeling the training data a multivariate gaussian for unimodal distributions and a mixture of gaussians model for multimodal distributions those probability densities are then used to formulate a maximum likelihood estimation framework for visual search and target detection for automatic object recognition and coding our learning technique is applied to the probabilistic visual modeling detection recognition and coding of human faces and nonrigid objects such as hands

erratum to existence and attractivity of almost periodic solutions for cellular neural networks with distributed delays and variable coefficients by combining the theory of the exponential dichotomy and liapunov function we study the existence and attractivity of almost periodic solutions for cellular neural networks cnns with distributed delays and variable coefficientsdx idt b i t x i t j 1 na i j t f j x j t j 1 nb i j t f j m j 0 k i j u x j t u du i i t we obtain some sufficient conditions to ensure that for the networks there exists a unique almost periodic solution and all its solutions converge to such an almost periodic solution an example is given to illustrate that the conditions of our results are feasible

joint distance and similarity measure learning based on triplet based constraints a novel model is proposed to learn combined distance and similarity measure cdsm based on triplet based constraints our cdsm can be kernelized for learning nonlinear measures two effective strategies are suggested to speed up the training and testing of kernelized cdsm extensive experiments validate the superiority of cdsm and kernelized cdsm distance and similarity measures usually are complementary to pattern classification with pairwise constraints several approaches have been proposed to combine distance and similarity measures however it remains less investigated to use triplets of samples for joint learning of distance and similarity measures moreover the kernel extension of triplet based model is also nontrivial and computationally expensive in this paper we propose a novel method to learn a combined distance and similarity measure cdsm by incorporating with the max margin model we suggest a triplet based cdsm learning model with a unified regularizer of the frobenius norm a support vector machine svm based algorithm is then adopted to solve the optimization problem furthermore we extend cdsm for learning nonlinear measures via the kernel trick two effective strategies are adopted to speed up training and testing of kernelized cdsm experiments on the uci handwritten digits and person re identification datasets demonstrate that cdsm and kernelized cdsm outperform several state of the art metric learning methods

linear neighborhood propagation and its applications in this paper a novel graph based transductive classification approach called linear neighborhood propagation is proposed the basic idea is to predict the label of a data point according to its neighbors in a linear way this method can be cast into a second order intrinsic gaussian markov random field framework its result corresponds to a solution to an approximate inhomogeneous biharmonic equation with dirichlet boundary conditions different from existing approaches our approach provides a novel graph structure construction method by introducing multiple wise edges instead of pairwise edges and presents an effective scheme to estimate the weights for such multiple wise edges to the best of our knowledge these two contributions are novel for semi supervised classification the experimental results on image segmentation and transductive classification demonstrate the effectiveness and efficiency of the proposed approach

principal manifolds and nonlinear dimensionality reduction via tangent space alignment we present a new algorithm for manifold learning and nonlinear dimensionality reduction based on a set of unorganized data points sampled with noise from a parameterized manifold the local geometry of the manifold is learned by constructing an approximation for the tangent space at each data point and those tangent spaces are then aligned to give the global coordinates of the data points with respect to the underlying manifold we also present an error analysis of our algorithm showing that reconstruction errors can be quite small in some cases we illustrate our algorithm using curves and surfaces both in two dimensional three dimensional 2d 3d euclidean spaces and in higher dimensional euclidean spaces we also address several theoretical and algorithmic issues for further research and improvements

pairwise constraints based multiview features fusion for scene classification recently we have witnessed a surge of interests of learning a low dimensional subspace for scene classification the existing methods do not perform well since they do not consider scenes multiple features from different views in low dimensional subspace construction in this paper we describe scene images by finding a group of features and explore their complementary characteristics we consider the problem of multiview dimensionality reduction by learning a unified low dimensional subspace to effectively fuse these features the new proposed method takes both intraclass and interclass geometries into consideration as a result the discriminability is effectively preserved because it takes into account neighboring samples which have different labels due to the semantic gap the fusion of multiview features still cannot achieve excellent performance of scene classification in real applications therefore a user labeling procedure is introduced in our approach initially a query image is provided by the user and a group of images are retrieved by a search engine after that users label some images in the retrieved set as relevant or irrelevant with the query the must links are constructed between the relevant images and the cannot links are built between the irrelevant images finally an alternating optimization procedure is adopted to integrate the complementary nature of different views with the user labeling information and develop a novel multiview dimensionality reduction method for scene classification experiments are conducted on the real world datasets of natural scenes and indoor scenes and the results demonstrate that the proposed method has the best performance in scene classification in addition the proposed method can be applied to other classification problems the experimental results of shape classification on caltech 256 suggest the effectiveness of our method highlights we describe scene images through multimodal features we explore the complementary characteristics of these features we address multimodal dimensionality reduction by learning a unified low dimensional subspace we adopt alternating optimization to integrate the features with the user labeling information we develop a novel multimodal dimensionality reduction method for scene classification

what is the goal of sensory coding a number of recent attempts have been made to describe early sensory coding in terms of a general information processing strategy in this paper two strategies are contrasted both strategies take advantage of the redundancy in the environment to produce more effective representations the first is described as a compact coding scheme a compact code performs a transform that allows the input to be represented with a reduced number of vectors cells with minimal rms error this approach has recently become popular in the neural network literature and is related to a process called principal components analysis pca a number of recent papers have suggested that the optimal compact code for representing natural scenes will have units with receptive field profiles much like those found in the retina and primary visual cortex however in this paper it is proposed that compact coding schemes are insufficient to account for the receptive field properties of cells in the mammalian visual pathway in contrast it is proposed that the visual system is near to optimal in representing natural scenes only if optimality is defined in terms of sparse distributed coding in a sparse distributed code all cells in the code have an equal response probability across the class of images but have a low response probability for any single image in such a code the dimensionality is not reduced rather the redundancy of the input is transformed into the redundancy of the firing pattern of cells it is proposed that the signature for a sparse code is found in the fourth moment of the response distribution i e the kurtosis in measurements with 55 calibrated natural scenes the kurtosis was found to peak when the bandwidths of the visual code matched those of cells in the mammalian visual cortex codes resembling wavelet transforms are proposed to be effective because the response histograms of such codes are sparse i e show high kurtosis when presented with natural scenes it is proposed that the structure of the image that allows sparse coding is found in the phase spectrum of the image it is suggested that natural scenes to a first approximation can be considered as a sum of self similar local functions the inverse of a wavelet possible reasons for why sensory systems would evolve toward sparse coding are presented

periodic solutions in an inhibitory two neuron network in this paper we consider a delayed network of two neurons with self feedback and interaction described by an all or none threshold function the discontinuity of signal function makes it difficult to apply directly dynamical system we show that the dynamics of the network can be understood in terms of the iterations of a one dimensional map and we obtain the existence and attractivity of periodic solutions moreover because the network is a limiting case of the corresponding smooth system as the parameter tends to infinity the above results can act as the guide to the rich dynamics of the smooth system therefore our results have important significance in both theory and applications

normalized cuts and image segmentation we propose a novel approach for solving the perceptual grouping problem in vision rather than focusing on local features and their consistencies in the image data our approach aims at extracting the global impression of an image we treat image segmentation as a graph partitioning problem and propose a novel global criterion the normalized cut for segmenting the graph the normalized cut criterion measures both the total dissimilarity between the different groups as well as the total similarity within the groups we show that an efficient computational technique based on a generalized eigenvalue problem can be used to optimize this criterion we applied this approach to segmenting static images as well as motion sequences and found the results to be very encouraging

boosted random contextual semantic space based representation for visual recognition we propose an image representation method using boosted random contextual semantic spaces the proposed brcss can alleviate the semantic gap by using semantic space based image representation brcss uses the visual representation of images in an iterative way with random sampling and re weighting experimental results demonstrate the effectiveness and efficiency of the proposed method visual information has been widely used for image representation although proven very effective the visual representation lacks explicit semantics however how to generate a proper semantic space for image representation is still an open problem that needs to be solved to jointly model the visual and semantic representations of images we propose a boosted random contextual semantic space based image representation method images are initially represented using local feature s distribution histograms the semantic space is generated by randomly selecting training images images are then mapped into the semantic space accordingly semantic context is explored to model the correlations of different semantics which is then used for classification the classification results are used to re weight training images in a boosted way the re weighted images are used to construct new semantic space for classification in this way we are able to jointly consider the visual and semantic information of images image classification experiments on several public datasets show the effectiveness of the proposed method

mean square global asymptotic stability of stochastic recurrent neural networks with distributed delays by constructing suitable lyapunov functionals and combining with matrix inequality technique a new simple sufficient condition is presented for the global asymptotic stability in the mean square of delayed neural networks

learning a mahalanobis distance metric for data clustering and classification distance metric is a key issue in many machine learning algorithms this paper considers a general problem of learning from pairwise constraints in the form of must links and cannot links as one kind of side information a must link indicates the pair of the two data points must be in a same class while a cannot link indicates that the two data points must be in two different classes given must link and cannot link information our goal is to learn a mahalanobis distance metric under this metric we hope the distances of point pairs in must links are as small as possible and those of point pairs in cannot links are as large as possible this task is formulated as a constrained optimization problem in which the global optimum can be obtained effectively and efficiently finally some applications in data clustering interactive natural image segmentation and face pose estimation are given in this paper experimental results illustrate the effectiveness of our algorithm

existence and stability of almost periodic solution for bam neural networks with delays by using the banach fixed point theorem and constructing suitable lyapunov function some sufficient conditions are obtained ensuring existence uniqueness and global stability of almost periodic solution of the bam neural networks with variable coefficients and delays these results are helpful to design global exponential stable bam networks and almost periodic oscillatory bam networks

filter based mean field inference for random fields with higher order terms and product label spaces recently a number of cross bilateral filtering methods have been proposed for solving multi label problems in computer vision such as stereo optical flow and object class segmentation that show an order of magnitude improvement in speed over previous methods these methods have achieved good results despite using models with only unary and or pairwise terms however previous work has shown the value of using models with higher order terms e g to represent label consistency over large regions or global co occurrence relations we show how these higher order terms can be formulated such that filter based inference remains possible we demonstrate our techniques on joint stereo and object labelling problems as well as object class segmentation showing in addition for joint object stereo labelling how our method provides an efficient approach to inference in product label spaces we show that we are able to speed up inference in these models around 10 30 times with respect to competing graph cut move making methods as well as maintaining or improving accuracy in all cases we show results on pascalvoc 10 for object class segmentation and leuven for joint object stereo labelling

almost automorphic solution for neutral type high order hopfield neural networks with delays in leakage terms on time scales abstract in this paper using the existence of the exponential dichotomy of linear dynamic equations on time scales a fixed point theorem and the theory of calculus on time scales some sufficient conditions for the existence and global exponential stability of almost automorphic solutions for a class of neutral type high order hopfield neural networks hhnns with delays in leakage terms on time scales are obtained finally a numerical example is provided to illustrate the feasibility of our results and our results also show that the continuous time neural network and its discrete time analogue have the same dynamical behaviors the results of this paper are completely new even when the time scale t r or z and show that the delays in the leakage term do harm to the existence and global exponential stability of almost automorphic solutions

improved results on robust exponential stability criteria for neutral type delayed neural networks abstract in this paper we investigate the problem of robust global exponential stability analysis for a class of neutral type neural networks the interval time varying delays allow for both slow and fast time varying delays the values of the time varying uncertain parameters are assumed to be bounded within given compact sets improved global exponential stability condition is derived by employing new lyapunov krasovskii functional and the integral inequality the developed nominal and robust stability criteria is delay dependent and characterized by linear matrix inequalities lmis the developed results are less conservative than previous published ones in the literature which are illustrated by representative numerical examples

automatic image annotation by semi supervised manifold kernel density estimation the insufficiency of labeled training data is a major obstacle in automatic image annotation to tackle this problem we propose a semi supervised manifold kernel density estimation ssmkde approach based on a recently proposed manifold kde method our contributions are twofold first ssmkde leverages both labeled and unlabeled samples and formulates all data in a manifold structure which enables a more accurate label prediction second the relationship between kde based methods and graph based semi supervised learning ssl methods is analyzed which helps to better understand graph based ssl methods extensive experiments demonstrate the superiority of ssmkde over existing kde based and graph based ssl methods

image classification by search with explicitly and implicitly semantic representations we model the explicitly and implicitly semantic representations for image classification explicitly semantic representations are obtained using learned models with supervision implicitly semantic representations are obtained with information from the internet the explicitly and implicitly semantic based method achieves good performances image classification refers to the task of automatically classifying the categories of images based on the contents this task is typically solved using visual features with the histogram based classification scheme although effective this strategy has two drawbacks on one hand histogram based representation often disregards the object layout which is very important for classification on the other hand visual features are unable to fully separate different images due to the semantic gap to solve these two problems in this paper we propose a novel image classification method by explicitly and implicitly representing the images with searching strategy first to make use of object layouts we randomly select a number of regions and then use these regions for image representations second we generate the explicitly semantic representations using a number of pre learned semantic models third we measure the visual similarities with the internet images and use the text information for implicitly semantic representations since internet images are contaminated with noise the resulting representations only implicitly reflect the contents of images finally both the explicitly and implicitly semantic representations are jointly modeled for image classifications by training bi linear classifiers we evaluate the effectiveness of the proposed image classification by search with explicitly and implicitly semantic representations method eisr on the scene 15 dataset the mit indoor dataset the uiuc sports dataset and the pascal voc 2007 dataset the experimental results prove the usefulness of the proposed method

a hyperspectral image classification framework and its application a new descriptor called lrp is proposed to describe subtle texture for 2d image texture enhancement is built to extend lrp to represent hsi a supervised manifold learning method called dlpp is proposed to reduce dimension the proposed framework outperforms other methods with experiments classification is a critical step to make full use of the hyperspectral data the most current approaches perform well for analyzing the macro texture but they often fail to deal with the micro texture thus this study proposes a general framework for the material with micro texture based on hyperspectral image hsi technique in this framework local response pattern lrp is firstly proposed to describe 2d image texture to preserve more structural information and keep less sensitive to image conditions then lrp is extended to represent hsi with texture enhancement te by considering opponent relationships between pairs of bands after that discriminated locality preserving projection dlpp is proposed to reduce data dimension in a linearizing nonlinear manifold way finally experiments on the hyperspectral images of fresh and frozen thawed fish fillets are conducted the results demonstrate that the proposed framework is efficient in terms of both recognition rates and robustness

texture recognition based on diffusion in networks a texture recognition method is proposed the image is modeled into networks the texture is characterized by the diffusion over the networks it is demonstrated that directed networks are better to texture recognition the proposed method outperform the state of the art much work has been done in the field of texture analysis and classification while promising classification methods have been proposed most of them rely on classical image analysis approaches this paper presents a texture classification method based on diffusion in directed networks first an image is modeled as a directed network by mapping each pixel as a node and connecting two nodes up to a maximum distance r to reveal texture properties links between two nodes are removed based on the pixel intensity difference once such a network is obtained the activity of each node is estimated by random walks and combined into a histogram to describe the image the main contribution of this paper is the use of directed networks which tends to provide better performance than in undirected cases also we have shown that the activity induced on these networks can be effectively used as texture descriptor experimental results show that the proposed method is favorably compared to traditional texture methods on widely used texture datasets the proposed method is also found to be promising for plant species classification using samples of leaf texture

semantic preserving distance metric learning and applications how do we accurately browse a large set of images or efficiently annotate the images from an image library image clustering methods are invaluable tools for applications such as content based image retrieval and image annotation to perform these tasks it is critical to have proper features to describe the visual and semantic content of images and to define an accurate distance metric to measure the dissimilarity between any two images however existing methods which adopt the features of color histograms edge direction histograms and shape context lack the ability to describe semantic content to solve this problem we propose a new approach that utilizes user provided pairwise constraints to describe the semantic relationship between two images a semantic preserving distance metric learning sp dml algorithm is developed to explore the complementary characteristics of the visual features and pairwise constraints in a unified feature space in this space the learned distance metric can be used to measure the dissimilarity between two images specifically the manifold structure adopted in sp dml is revealed by the image s visual features to integrate semantic contents in distance metric learning sp dml utilizes pairwise constraints to build semantic patches and align these patches to obtain the optimal distance metric for the new feature space experimental results in image clustering demonstrate that the performance of sp dml is appealing

spatial multi scale gradient orientation consistency for place instance and scene category recognition gradient orientation consistency goc was proposed to encode local structure of images the goc feature is more robust to illumination variances the spatial multi scale goc histogram could achieve competitive performance for both place instance and scene category recognition this paper presents a novel image feature called gradient orientation consistency goc and an image representation based on the spatial multi scale goc histogram for place instance and scene category recognition gradient orientations have proven to be a more robust descriptor of the image information than intensities in the presence of illumination variances the goc feature is obtained by imposing a local binary pattern lbp type of non parametric operator to the gradient orientation codes hence captures the local structures in an image the histogram of goc further encodes the global structure of the entire image the multi scale analysis and spatial pyramid techniques are also used to enrich the longer range structure information resulting in a comprehensive image representation we apply this representation method for place instance and scene category recognition on five different datasets and show that it outperforms the state of the art

global stability of almost periodic solutions of hopfield neural networks with neutral time varying delays in this paper the global stability and almost periodicity are investigated for hopfield neural networks with neutral time varying delays some sufficient conditions are obtained for the existence and globally exponential stability of almost periodic solution by employing fixed point theorem and differential inequality techniques the results of this paper are new and they complement previously known results

towards robust discriminative projections learning via non greedy l2 1 norm minmax linear discriminant analysis lda is one of the most successful supervised dimensionality reduction methods and has been widely used in many real world applications however the l 2 norm is employed as the distance measure in objective of lda which is sensitive to outliers many previous works improve the robustness of lda by using l1 norm distance however the robustness against outliers is limited and the solver of l1 norm are mostly based on greedy search strategy which is time consuming and easy to get stuck in a local optimum in this paper we propose a novel robust lda measured by l 2 1 norm to learn robust discriminative projections since the proposed model needs to minimize and maximize minmax l 2 1 norm terms simultaneously it is challenging to solve as a result we first systematically derive an efficient iterative optimization algorithm to solve a general ratio minimization problem and rigorously prove its convergence more importantly an efficient non greedy iterative re weighted optimization algorithm is developed based on preceding approach for solving proposed l 2 1 norm minmax problem besides an optimal weighted mean mechanism is driven according to designed objective and solver which can be applied to other approach for robustness improvement extensive experimental results on several real world datasets show the effectiveness of proposed method

can adaption help on the average we study adaptive information for approximation of linear problems in a separable hilbert space equipped with a probability measure it is known that adaption does not help in the worst case for linear problems we prove that adaption also doesnot help on the average that is there exists nonadaptive information which is as powerful as adaptive information this result holds for orthogonally invariant measures we provide necessary and sufficient conditions for a measure to be orthogonally invariant examples of orthogonally invariant measures include gaussian measures and in the finite dimensional case weighted lebesgue measures

adaptive hypergraph learning and its application in image classification recent years have witnessed a surge of interest in graph based transductive image classification existing simple graph based transductive learning methods only model the pairwise relationship of images however and they are sensitive to the radius parameter used in similarity calculation hypergraph learning has been investigated to solve both difficulties it models the high order relationship of samples by using a hyperedge to link multiple samples nevertheless the existing hypergraph learning methods face two problems i e how to generate hyperedges and how to handle a large set of hyperedges this paper proposes an adaptive hypergraph learning method for transductive image classification in our method we generate hyperedges by linking images and their nearest neighbors by varying the size of the neighborhood we are able to generate a set of hyperedges for each image and its visual neighbors our method simultaneously learns the labels of unlabeled images and the weights of hyperedges in this way we can automatically modulate the effects of different hyperedges thorough empirical studies show the effectiveness of our approach when compared with representative baselines

error bounds of multi graph regularized semi supervised classification in this paper we investigate the generalization performance of the multi graph regularized semi supervised classification algorithm associated with the hinge loss we provide estimates for the excess misclassification error of multi graph regularized classifiers and show the relations between the generalization performance and the structural invariants of data graphs experiments performed on real database demonstrate the effectiveness of our theoretical analysis

new results for global stability of a class of neutral type neural systems with time delays this paper studies the global convergence properties of a class of neutral type neural networks with discrete time delays this class of neutral systems includes cohen grossberg neural networks hopfield neural networks and cellular neural networks based on the lyapunov stability theorems some delay independent sufficient conditions for the global asymptotic stability of the equilibrium point for this class of neutral type systems are derived it is shown that the results presented in this paper for neutral type delayed neural networks are the generalization of a recently reported stability result a numerical example is also given to demonstrate the applicability of our proposed stability criteria

improved global robust stability of interval delayed neural networks via split interval generalizations abstract the problem of global robust stability of hopfield type delayed neural networks with the intervalized network parameters is revisited recently a computationally tractable i e linear matrix inequality lmi based global robust stability criterion derived from an earlier criterion based on dividing the given interval into more that two intervals has been presented in the present paper generalizations i e division of the given interval into m intervals where m is an integer greater than or equal to 2 is considered and some new lmi based global robust stability criteria are derived it is shown that in some cases m 2 may not suffice i e m 2 may be needed to realize the improvement an example showing the effectiveness of the proposed generalization is given the paper also provides a complete and systematic explanation of the split interval idea

local structure preserving discriminative projections for rgb d sensor based scene classification rgb d sensor based scene classification has recently received attention because of its potential use in human computer interaction applications however when feature representation schemes are adopted the dimensionality of concatenated scene features extracted from the rgb and depth image pair is very high and faces the curse of dimensionality therefore we present a new subspace selection scheme called local structure preserving discriminative projections lspdp lspdp simultaneously considers two issues 1 achieving a balance between the local patch structure and global within class structure and 2 maximizing between class distances extensive experimentation on the nyu depth v1 dataset with feature sets computed using three popular schemes locality constrained linear coding llc spatial pyramid matching using sparse coding sc spm and efficient match kernels emks demonstrates the robustness and effectiveness of the new method for rgb d sensor based scene classification

solving partially observable problems with inaccurate psr models modeling dynamical systems is a commonly used technique to solve partially observable problems in the artificial intelligence field predictive state representations psrs have been proposed as an alternative to partially observable markov decision processes pomdps to model dynamical systems although pomdps and psrs provide general frameworks to solve partially observable problems they rely heavily on a known and accurate model of the environment however in real world applications it is extremely difficult to build an accurate model in this paper we propose an algorithm to solve partially observable problems using an inaccurate psr model which is learned from samples the proposed algorithm can also improve the accuracy of the learned model given the inaccurate psr model the psr state is identified firstly then the traditional markov decision processes mdp techniques are used to solve the partially observable problem furthermore the learned model which may get off track as often happens when the model is learned from samples can be reset the effectiveness of our proposed algorithm is demonstrated based on a standard set of pomdp test problems

statistical estimation and optimal recovery new formulas are given for the minimax linear risk in estimating a linear functional of an unknown object from indirect data contaminated with random gaussian noise the formulas cover a variety of loss functions and do not require the symmetry of the convex a priori class it is shown that affine minimax rules are within a few percent of minimax even among nonlinear rules for a variety of loss functions it is also shown that difficulty of estimation is measured by the modulus of continuity of the functional to be estimated the method of proof exposes a correspondence between minimax affine estimates in the statistical estimation problem and optimal algorithms in the theory of optimal recovery

multi label ensemble based on variable pairwise constraint projection multi label classification has attracted an increasing amount of attention in recent years to this end many algorithms have been developed to classify multi label data in an effective manner however they usually do not consider the pairwise relations indicated by sample labels which actually play important roles in multi label classification inspired by this we naturally extend the traditional pairwise constraints to the multi label scenario via a flexible thresholding scheme moreover to improve the generalization ability of the classifier we adopt a boosting like strategy to construct a multi label ensemble from a group of base classifiers to achieve these goals this paper presents a novel multi label classification framework named variable pairwise constraint projection for multi label ensemble vpcme specifically we take advantage of the variable pairwise constraint projection to learn a lower dimensional data representation which preserves the correlations between samples and labels thereafter the base classifiers are trained in the new data space for the boosting like strategy we employ both the variable pairwise constraints and the bootstrap steps to diversify the base classifiers empirical studies have shown the superiority of the proposed method in comparison with other approaches

learning dictionary of discriminative part detectors for image categorization and cosegmentation this paper proposes a novel approach to learning mid level image models for image categorization and cosegmentation we represent each image class by a dictionary of part detectors that best discriminate that class from the background we learn category specific part detectors in a weakly supervised setting in which the training images are only annotated with category labels without part object location information we use a latent svm model regularized using the ell 2 1 l2 1 group sparsity norm to learn the part detectors starting from a large set of initial parts the group sparsity regularizer forces the model to jointly select and optimize a set of discriminative part detectors in a max margin framework we propose a stochastic version of a proximal algorithm to solve the corresponding optimization problem we apply the learned part detectors to image classification and cosegmentation and present extensive comparative experiments with standard benchmarks

supervised discrete hashing with relaxation data dependent hashing has recently attracted attention due to being able to support efficient retrieval and storage of high dimensional data such as documents images and videos in this paper we propose a novel learning based hashing method called supervised discrete hashing with relaxation sdhr based on supervised discrete hashing sdh sdh uses ordinary least squares regression and traditional zero one matrix encoding of class label information as the regression target code words thus fixing the regression target in sdhr the regression target is instead optimized the optimized regression target matrix satisfies a large margin constraint for correct classification of each example compared with sdh which uses the traditional zero one matrix sdhr utilizes the learned regression target matrix and therefore more accurately measures the classification error of the regression model and is more flexible as expected sdhr generally outperforms sdh experimental results on two large scale image data sets cifar 10 and mnist and a large scale and challenging face data set frgc demonstrate the effectiveness and efficiency of sdhr

exploiting pairwise recommendation and clustering strategies for image re ranking in content based image retrieval cbir systems accurately ranking collection images is of great relevance users are interested in the returned images placed at the first positions which usually are the most relevant ones commonly image content descriptors are used to compute ranked lists in cbir systems in general these systems perform only pairwise image analysis that is compute similarity measures considering only pairs of images ignoring the rich information encoded in the relations among several images this paper presents a novel re ranking approach used to improve the effectiveness of cbir tasks by exploring relations among images in our approach a recommendation based strategy is combined with a clustering method both exploit contextual information encoded in ranked lists computed by cbir systems we conduct several experiments to evaluate the proposed method our experiments consider shape color and texture descriptors and comparisons with other post processing methods experimental results demonstrate the effectiveness of our method

rank minimization with applications to image noise removal abstract rank minimization problem has a wide range of applications in different areas however since this problem is np hard and non convex the frequently used method is to replace the matrix rank minimization with nuclear norm minimization nuclear norm is the convex envelope of the matrix rank and it is more computationally tractable matrix completion is a special case of rank minimization problem in this paper we consider directly using matrix rank as the regularization term instead of nuclear norm in the cost function for matrix completion problem the solution is analyzed and obtained by a hard thresholding operation on the singular values of the observed matrix then by exploiting patch based nonlocal self similarity scheme we apply the proposed rank minimization algorithm to remove white gaussian additive noise in images gamma multiplicative noise is also removed in logarithm domain the experimental results illustrate that the proposed algorithm can remove noises in images more efficiently than nuclear norm can do and the results are also competitive with those obtained by using the existing state of the art noise removal methods in the literature

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

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

manifold regularized cross modal embedding for zero shot learning zero shot learning zsl aims at classifying previously unseen class samples and has gained its popularity in applications where samples of some categories are scarce for training the basic idea to address this issue is transferring knowledge from the seen classes to the unseen classes through mapping the visual feature to an embedding space spanned by class semantic information the class semantic information can be obtained from human labeled attributes or text corpus in an unsupervised fashion therefore the embedding function from visual space to the embedding space is extremely important however the existing embedding approaches to zsl mainly focus on aligning pairwise semantic consistency from heterogeneous spaces but ignore the intrinsic structure of the locally homogeneous isomorph in order to preserve the locally visual structure in the embedding process this paper proposes a manifold regularized cross modal embedding mcme approach for zsl by formulating the manifold constraint for intrinsic structure of the visual features as well as aligning pairwise consistency the linear closed form solution makes mcme efficient to compute furthermore rather than applying the embedding function learned from the seen classes directly we also propose a new domain adaptation strategy to overcome the domain shift problem during the knowledge transfer process the mcme with the domain adaptation method is called mcme da extensive experiments on the benchmark datasets of awa and cub validate the superiority and promise of mcme and mcme da

distance metric learning for large margin nearest neighbor classification the accuracy of k nearest neighbor knn classification depends significantly on the metric used to compute distances between different examples in this paper we show how to learn a mahalanobis distance metric for knn classification from labeled examples the mahalanobis metric can equivalently be viewed as a global linear transformation of the input space that precedes knn classification using euclidean distances in our approach the metric is trained with the goal that the k nearest neighbors always belong to the same class while examples from different classes are separated by a large margin as in support vector machines svms the margin criterion leads to a convex optimization based on the hinge loss unlike learning in svms however our approach requires no modification or extension for problems in multiway as opposed to binary classification in our framework the mahalanobis distance metric is obtained as the solution to a semidefinite program on several data sets of varying size and difficulty we find that metrics trained in this way lead to significant improvements in knn classification sometimes these results can be further improved by clustering the training examples and learning an individual metric within each cluster we show how to learn and combine these local metrics in a globally integrated manner

existence and global exponential stability of periodic solution of cellular neural networks with time varying delays by using the continuation theorem of mawhin s coincidence degree theory and gronwall s inequality some new sufficient conditions are obtained ensuring existence and global exponential stability of periodic solution of cellular neural networks with periodic coefficients and delays these results are helpful to design globally exponentially stable and oscillatory cellular neural networks

generalization performance of magnitude preserving semi supervised ranking with graph based regularization semi supervised ranking is a relatively new and important learning problem inspired by many applications we propose a novel graph based regularized algorithm which learns the ranking function in the semi supervised learning framework it can exploit geometry of the data while preserving the magnitude of the preferences the least squares ranking loss is adopted and the optimal solution of our model has an explicit form we establish error analysis of our proposed algorithm and demonstrate the relationship between predictive performance and intrinsic properties of the graph the experiments on three datasets for recommendation task and two quantitative structure activity relationship datasets show that our method is effective and comparable to some other state of the art algorithms for ranking

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

image level classification by hierarchical structure learning with visual and semantic similarities abstract image classification methods often use class level information without considering the distinctive character of each image images of the same class may have varied appearances besides visually similar images may not be semantically correlated to solve these problems in this paper we propose a novel image classification method by automatically learning the image level hierarchical structure ilhs using both visual and semantic similarities we try to generate new representations by exploring both visual and semantic similarities of images images are clustered hierarchically to explore their correlations we then use them for image representations the diversity of image classes within each cluster is used to re weight visual similarities the re weighted similarities are aggregated to generate new image representations we conduct image classification experiments on the caltech 256 dataset the pascal voc 2007 dataset and the pascal voc 2012 dataset experimental results demonstrate the effectiveness of the proposed method

distance metrics for high dimensional nearest neighborhood recovery compression and normalization previous work has shown that the minkowski p distance metrics are unsuitable for clustering very high dimensional document data we extend this work we frame statistical theory on the relationships between the euclidean cosine and correlation distance metrics in terms of item neighborhoods we discuss the differences between the cosine and correlation distance metrics and illustrate our discussion with an example from collaborative filtering we introduce a family of normalized minkowski metrics and test their use on both document data and synthetic data generated from the uniform distribution we describe a range of criteria for testing neighborhood homogeneity relative to underlying latent classes we discuss how these criteria are explicitly and implicitly linked to classification performance by testing both normalized and non normalized minkowski p metrics for multiple values of p we separate out distance compression effects from normalization effects for multi class classification problems we believe that distance compression on high dimensional data aids classification and data analysis for document data we find that the cosine and normalized euclidean correlation and proportioned city block metrics give strong neighborhood recovery the proportioned city block metric gives particularly good results for nearest neighbors recovery and should be used when utilizing document data analysis techniques for which nearest neighborhood recovery is important for data generated from the uniform distribution neighborhood recovery improves as the value of p increases

tensor rank preserving discriminant analysis for facial recognition facial recognition one of the basic topics in computer vision and pattern recognition has received substantial attention in recent years however for those traditional facial recognition algorithms the facial images are reshaped to a long vector thereby losing part of the original spatial constraints of each pixel in this paper a new tensor based feature extraction algorithm termed tensor rank preserving discriminant analysis trpda for facial image recognition is proposed the proposed method involves two stages in the first stage the low dimensional tensor subspace of the original input tensor samples was obtained in the second stage discriminative locality alignment was utilized to obtain the ultimate vector feature representation for subsequent facial recognition on the one hand the proposed trpda algorithm fully utilizes the natural structure of the input samples and it applies an optimization criterion that can directly handle the tensor spectral analysis problem thereby decreasing the computation cost compared those traditional tensor based feature selection algorithms on the other hand the proposed trpda algorithm extracts feature by finding a tensor subspace that preserves most of the rank order information of the intra class input samples experiments on the three facial databases are performed here to determine the effectiveness of the proposed trpda algorithm

correlated logistic model with elastic net regularization for multilabel image classification in this paper we present correlated logistic corrlog model for multilabel image classification corrlog extends conventional logistic regression model into multilabel cases via explicitly modeling the pairwise correlation between labels in addition we propose to learn the model parameters of corrlog with elastic net regularization which helps exploit the sparsity in feature selection and label correlations and thus further boost the performance of multilabel classification corrlog can be efficiently learned though approximately by regularized maximum pseudo likelihood estimation and it enjoys a satisfying generalization bound that is independent of the number of labels corrlog performs competitively for multilabel image classification on benchmark data sets mulan scene mit outdoor scene pascal voc 2007 and pascal voc 2012 compared with the state of the art multilabel classification algorithms

what does the retina know about natural scenes by examining the experimental data on the statistical properties of natural scenes together with retinal contrast sensitivity data we arrive at a first principle theoretical hypothesis for the purpose of retinal processing and its relationship to an animal s environment we argue that the retinal goal is to transform the visual input as much as possible into a statistically independent basis as the first step in creating a redundancy reduced representation in the cortex as suggested by barlow the extent of this whitening of the input is limited however by the need to suppress input noise our explicit theoretical solutions for the retinal filters also show a simple dependence on mean stimulus luminance they predict an approximate weber law at low spatial frequencies and a de vries rose law at high frequencies assuming that the dominant source of noise is quantum we generate a family of contrast sensitivity curves as a function of mean luminance this family is compared to psychophysical data

do linear problems have linear optimal algorithms a selective introduction to the field of information based complexity is presented in the context of the question raised in the title after introducing some of the basic ideas of this relatively new area that interfaces mathematics and theoretical computer science the article surveys results on the existence of linear optimal algorithms for linear problems several illustrative examples are given along with an indication of the variety of disciplines in which techniques of information based complexity are being applied known under aliases such as the general theory of optimal algorithms and the information centered approach to algorithms information based complexity seeks to create a general theory about problems with partial or approximate information and to apply the results to specific problems some of the disciplines where problems with such incomplete information have arisen include computer science economics control theory signal processing and geophysics the goal of this article is to present a selective introduction to the field of information based complexity and to describe the seesaw nature of results in the last decade on the extent to which linear problems have linear optimal algorithms the approach we take will certainly not do justice to the breadth of ideas techniques and applications that are coming into play in the general theory instead we will take a direct route to the results on linear optimal algorithms pausing occasionally to survey the pleasing way in which ideas from linear algebra and functional analysis are used in the final section we mention some other theoretical and applied topics currently under investigation in information based complexity a more general expository overview can be found in traub and wozniakowski 15 and more technical surveys appear in packel and wozniakowski 10 and wozniakowski 21 2 two preliminary examples we set the stage for an exposition of the general linear theory by imposing partial information on two problems which are known to virtually every student of mathematics the problem of integrating a continuous function needs no introduction the fourier series approach to approximating func tions by linear combinations of sines and cosines is almost as familiar we do not develop these ideas as a testimony to the power of the general theory more about that later but rather to have a comfortable base to refer to when the generalizations and abstractions are introduced integration example let f be a function from the space w 0 1 of real valued continuous functions on the interval 0 1 we would like to obtain a value for the

two stage multiple kernel learning for supervised dimensionality reduction in supervised dimensionality reduction methods for pattern recognition tasks the information of the class labels is considered through the process of reducing the input dimensionality to improve the classification accuracy using nonlinear mappings for this purpose makes these models more appropriate for nonlinearly distributed data in this paper a new nonlinear supervised dimensionality reduction model is introduced the dimensionality reduction process in this model is performed through a multiple kernel learning paradigm in two stages in the first stage three suitable criteria for supervised dimensionality reduction containing fisher homoscedasticity and between class distance criteria are used to find the kernel weights with these weights a linear combination of several valid kernels is computed to make a new suitable kernel function in the second stage the kernel discriminant analysis method is employed for nonlinear supervised dimensionality reduction using the kernel computed in the first stage many experiments on a variety of real world datasets including handwritten digits images objects images and other datasets show that the proposed approach among a number of well known related techniques results in accurate and fast classifications a novel approach for non linear supervised dimensionality reduction is proposed the proposed method uses kernel discriminant analysis method with new kernels a multiple kernel learning mkl paradigm produces the new kernel function proper criteria for supervised dimensionality reduction are optimized in the mkl part the proposed method is successfully evaluated using many real world datasets

multimodal graph based reranking for web image search this paper introduces a web image search reranking approach that explores multiple modalities in a graph based learning scheme different from the conventional methods that usually adopt a single modality or integrate multiple modalities into a long feature vector our approach can effectively integrate the learning of relevance scores weights of modalities and the distance metric and its scaling for each modality into a unified scheme in this way the effects of different modalities can be adaptively modulated and better reranking performance can be achieved we conduct experiments on a large dataset that contains more than 1000 queries and 1 million images to evaluate our approach experimental results demonstrate that the proposed reranking approach is more robust than using each individual modality and it also performs better than many existing methods

lmi conditions for global asymptotic stability results for neutral type neural networks with distributed time delays abstract in this paper the global asymptotic stability of neutral type neural networks with unbounded distributed delays is analyzed by utilizing the lyapunov krasovskii functional and the linear matrix inequality lmi approach a new sufficient condition ensuring the global asymptotic stability for neutral type neural networks is obtained by using the powerful matlab lmi toolbox two examples are provided to illustrate the applicability of the stability results

semisupervised multiview distance metric learning for cartoon synthesis in image processing cartoon character classification retrieval and synthesis are critical so that cartoonists can effectively and efficiently make cartoons by reusing existing cartoon data to successfully achieve these tasks it is essential to extract visual features that comprehensively represent cartoon characters and to construct an accurate distance metric to precisely measure the dissimilarities between cartoon characters in this paper we introduce three visual features color histogram shape context and skeleton to characterize the color shape and action respectively of a cartoon character these three features are complementary to each other and each feature set is regarded as a single view however it is improper to concatenate these three features into a long vector because they have different physical properties and simply concatenating them into a high dimensional feature vector will suffer from the so called curse of dimensionality hence we propose a semisupervised multiview distance metric learning ssm dml ssm dml learns the multiview distance metrics from multiple feature sets and from the labels of unlabeled cartoon characters simultaneously under the umbrella of graph based semisupervised learning ssm dml discovers complementary characteristics of different feature sets through an alternating optimization based iterative algorithm therefore ssm dml can simultaneously accomplish cartoon character classification and dissimilarity measurement on the basis of ssm dml we develop a novel system that composes the modules of multiview cartoon character classification multiview graph based cartoon synthesis and multiview retrieval based cartoon synthesis experimental evaluations based on the three modules suggest the effectiveness of ssm dml in cartoon applications

ordinal margin metric learning and its extension for cross distribution image data propose a novel ordinal margin metric learning ormml by separating the data with large margins and making them distributed in order extend ormml to cope with cross distribution application scenarios named cd ormml extensively demonstrate the superiority of the proposed metric learning methods to related state of the art methods in machine learning and computer vision fields a wide range of applications such as human age estimation and head pose recognition are related to ordinal data in which there exists an order relationship to perform such ordinal estimations in a desired metric space in this paper we first propose a novel ordinal margin metric learning ormml method by separating the data classes with a sequence of margins which makes the classes distribute orderly in the learned metric space then to cope with more realistic scenarios where the data are sampled with each class across multiple distributions we present a cross distribution variant of ormml coined as cd ormml by maximizing the correlation between distributions within each class when conducting metric learning finally extensive experiments on synthetic and publicly available image datasets demonstrate the superiority of the proposed methods in performance to the state of the art methods