beyond diffusion process nss is proposed to replace the role of diffusion process to capture the geometry of the underlying manifold in shape and image retrieval nss is more precise than diffusion process and more robust to noise nss is computed more efficiently than diffusion process and it no longer needs an iterative process to guarantee the retrieval precision we obtain state of the art retrieval performance on several benchmark datasets measuring the similarity between two instances reliably shape or image is a challenging problem in shape and image retrieval in this paper a simple yet effective method called neighbor set similarity nss is proposed which is superior to both traditional pairwise similarity and diffusion process nss makes full use of contextual information to capture the geometry of the underlying manifold and obtains a more precise measure than the original pairwise similarity moreover based on nss we propose a powerful fusion process to utilize the complementarity of different descriptors to further enhance the retrieval performance the experimental results on mpeg 7 shape dataset n s image dataset and orl face dataset demonstrate the effectiveness of the proposed method in addition the time complexity of nss is much lower than diffusion process which suggests that nss is more suitable for large scale image retrieval than diffusion process

nonlocal discrete regularization on weighted graphs a framework for image and manifold processing we introduce a nonlocal discrete regularization framework on weighted graphs of the arbitrary topologies for image and manifold processing the approach considers the problem as a variational one which consists of minimizing a weighted sum of two energy terms a regularization one that uses a discrete weighted dirichlet energy and an approximation one this is the discrete analogue of recent continuous euclidean nonlocal regularization functionals the proposed formulation leads to a family of simple and fast nonlinear processing methods based on the weighted laplace operator parameterized by the degree of regularity the graph structure and the graph weight function these discrete processing methods provide a graph based version of recently proposed semi local or nonlocal processing methods used in image and mesh processing such as the bilateral filter the tv digital filter or the nonlocal means filter it works with equal ease on regular 2 d and 3 d images manifolds or any data we illustrate the abilities of the approach by applying it to various types of images meshes manifolds and data represented as graphs

active reranking for web image search image search reranking methods usually fail to capture the user s intention when the query term is ambiguous therefore reranking with user interactions or active reranking is highly demanded to effectively improve the search performance the essential problem in active reranking is how to target the user s intention to complete this goal this paper presents a structural information based sample selection strategy to reduce the user s labeling efforts furthermore to localize the user s intention in the visual feature space a novel local global discriminative dimension reduction algorithm is proposed in this algorithm a submanifold is learned by transferring the local geometry and the discriminative information from the labelled images to the whole global image database experiments on both synthetic datasets and a real web image search dataset demonstrate the effectiveness of the proposed active reranking scheme including both the structural information based active sample selection strategy and the local global discriminative dimension reduction algorithm

large scale multiple kernel learning while classical kernel based learning algorithms are based on a single kernel in practice it is often desirable to use multiple kernels lanckriet et al 2004 considered conic combinations of kernel matrices for classification leading to a convex quadratically constrained quadratic program we show that it can be rewritten as a semi infinite linear program that can be efficiently solved by recycling the standard svm implementations moreover we generalize the formulation and our method to a larger class of problems including regression and one class classification experimental results show that the proposed algorithm works for hundred thousands of examples or hundreds of kernels to be combined and helps for automatic model selection improving the interpretability of the learning result in a second part we discuss general speed up mechanism for svms especially when used with sparse feature maps as appear for string kernels allowing us to train a string kernel svm on a 10 million real world splice data set from computational biology we integrated multiple kernel learning in our machine learning toolbox shogun for which the source code is publicly available at http www fml tuebingen mpg de raetsch projects shogun

multiview hessian regularization for image annotation the rapid development of computer hardware and internet technology makes large scale data dependent models computationally tractable and opens a bright avenue for annotating images through innovative machine learning algorithms semisupervised learning ssl therefore received intensive attention in recent years and was successfully deployed in image annotation one representative work in ssl is laplacian regularization lr which smoothes the conditional distribution for classification along the manifold encoded in the graph laplacian however it is observed that lr biases the classification function toward a constant function that possibly results in poor generalization in addition lr is developed to handle uniformly distributed data or single view data although instances or objects such as images and videos are usually represented by multiview features such as color shape and texture in this paper we present multiview hessian regularization mhr to address the above two problems in lr based image annotation in particular mhr optimally combines multiple hr each of which is obtained from a particular view of instances and steers the classification function that varies linearly along the data manifold we apply mhr to kernel least squares and support vector machines as two examples for image annotation extensive experiments on the pascal voc 07 dataset validate the effectiveness of mhr by comparing it with baseline algorithms including lr and hr

stabilization and disturbance rejection for the beam equation we consider a system described by the one dimensional linear wave equation in a bounded domain with appropriate boundary conditions to stabilize the system we propose a dynamic boundary controller applied at the free end of the system the transfer function of the proposed controller is a proper rational function of the complex variable and may contain a single pole at the origin and a pair of complex conjugate poles on the imaginary axis provided that the residues corresponding to these poles are nonnegative the rest of the transfer function is required to be a strictly positive real function we then show that depending on the location of the pole on the imaginary axis the closed loop system is asymptotically stable we also consider the case where the output of the controller is corrupted by a disturbance and show that it may be possible to attenuate the effect of the disturbance at the output if we choose the controller transfer function appropriately we also present some numerical simulation results which support this argument

shape retrieval using hierarchical total bregman soft clustering in this paper we consider the family of total bregman divergences tbds as an efficient and robust distance measure to quantify the dissimilarity between shapes we use the tbd based l1 norm center as the representative of a set of shapes and call it the t center first we briefly present and analyze the properties of the tbds and t centers following our previous work in 1 then we prove that for any tbd there exists a distribution which belongs to the lifted exponential family lef of statistical distributions further we show that finding the maximum a posteriori map estimate of the parameters of the lifted exponential family distribution is equivalent to minimizing the tbd to find the t centers this leads to a new clustering technique namely the total bregman soft clustering algorithm we evaluate the tbd t center and the soft clustering algorithm on shape retrieval applications our shape retrieval framework is composed of three steps 1 extraction of the shape boundary points 2 affine alignment of the shapes and use of a gaussian mixture model gmm 2 3 4 to represent the aligned boundaries and 3 comparison of the gmms using tbd to find the best matches given a query shape to further speed up the shape retrieval algorithm we perform hierarchical clustering of the shapes using our total bregman soft clustering algorithm this enables us to compare the query with a small subset of shapes which are chosen to be the cluster t centers we evaluate our method on various public domain 2d and 3d databases and demonstrate comparable or better results than state of the art retrieval techniques

online nonnegative matrix factorization with robust stochastic approximation nonnegative matrix factorization nmf has become a popular dimension reduction method and has been widely applied to image processing and pattern recognition problems however conventional nmf learning methods require the entire dataset to reside in the memory and thus cannot be applied to large scale or streaming datasets in this paper we propose an efficient online rsa nmf algorithm or nmf that learns nmf in an incremental fashion and thus solves this problem in particular or nmf receives one sample or a chunk of samples per step and updates the bases via robust stochastic approximation benefitting from the smartly chosen learning rate and averaging technique or nmf converges at the rate of in each update of the bases furthermore we prove that or nmf almost surely converges to a local optimal solution by using the quasi martingale by using a buffering strategy we keep both the time and space complexities of one step of the or nmf constant and make or nmf suitable for large scale or streaming datasets preliminary experimental results on real world datasets show that or nmf outperforms the existing online nmf onmf algorithms in terms of efficiency experimental results of face recognition and image annotation on public datasets confirm the effectiveness of or nmf compared with the existing onmf algorithms

manifold regularization a geometric framework for learning from labeled and unlabeled examples we propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution we focus on a semi supervised framework that incorporates labeled and unlabeled data in a general purpose learner some transductive graph learning algorithms and standard methods including support vector machines and regularized least squares can be obtained as special cases we use properties of reproducing kernel hilbert spaces to prove new representer theorems that provide theoretical basis for the algorithms as a result in contrast to purely graph based approaches we obtain a natural out of sample extension to novel examples and so are able to handle both transductive and truly semi supervised settings we present experimental evidence suggesting that our semi supervised algorithms are able to use unlabeled data effectively finally we have a brief discussion of unsupervised and fully supervised learning within our general framework

contextual object localization with multiple kernel nearest neighbor recently many object localization models have shown that incorporating contextual cues can greatly improve accuracy over using appearance features alone therefore many of these models have explored different types of contextual sources but only considering one level of contextual interaction at the time thus what context could truly contribute to object localization through integrating cues from all levels simultaneously remains an open question moreover the relative importance of the different contextual levels and appearance features across different object classes remains to be explored here we introduce a novel framework for multiple class object localization that incorporates different levels of contextual interactions we study contextual interactions at the pixel region and object level based upon three different sources of context semantic boundary support and contextual neighborhoods our framework learns a single similarity metric from multiple kernels combining pixel and region interactions with appearance features and then applies a conditional random field to incorporate object level interactions to effectively integrate different types of feature descriptions we extend the large margin nearest neighbor to a novel algorithm that supports multiple kernels we perform experiments on three challenging image databases graz 02 msrc and pascal voc 2007 experimental results show that our model outperforms current state of the art contextual frameworks and reveals individual contributions for each contextual interaction level as well as appearance features indicating their relative importance for object localization

general tensor discriminant analysis and gabor features for gait recognition traditional image representations are not suited to conventional classification methods such as the linear discriminant analysis lda because of the undersample problem usp the dimensionality of the feature space is much higher than the number of training samples motivated by the successes of the two dimensional lda 2dlda for face recognition we develop a general tensor discriminant analysis gtda as a preprocessing step for lda the benefits of gtda compared with existing preprocessing methods such as the principal components analysis pca and 2dlda include the following 1 the usp is reduced in subsequent classification by for example lda 2 the discriminative information in the training tensors is preserved and 3 gtda provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of gtda converges whereas that of 2dlda does not we use human gait recognition to validate the proposed gtda the averaged gait images are utilized for gait representation given the popularity of gabor function based image decompositions for image understanding and object recognition we develop three different gabor function based image representations 1 gabord is the sum of gabor filter responses over directions 2 gabors is the sum of gabor filter responses over scales and 3 gaborsd is the sum of gabor filter responses over scales and directions the gabord gabors and gaborsd representations are applied to the problem of recognizing people from their averaged gait images a large number of experiments were carried out to evaluate the effectiveness recognition rate of gait recognition based on first obtaining a gabor gabord gabors or gaborsd image representation then using gdta to extract features and finally using lda for classification the proposed methods achieved good performance for gait recognition based on image sequences from the university of south florida usf humanid database experimental comparisons are made with nine state of the art classification methods in gait recognition

learning context sensitive similarity by shortest path propagation in this paper we introduce a novel shape object retrieval algorithm shortest path propagation ssp given a query object q and a target database object p we explicitly find the shortest path between them in the distance manifold of the database objects then a new distance measure between q and p is learned based on the database objects on the shortest path to replace the original distance measure the promising results on both mepg 7 shape dataset and a protein dataset demonstrate that our method can significantly improve the ranking of the object retrieval

a dynamic control law for the wave equation abstract we consider a system described by the one dimensional linear wave equation in a bounded domain with appropriate boundary conditions to stabilize the system we propose a dynamic boundary controller applied at the free end of the system the transfer function of the proposed controller is restricted to be a positive real function which could be strictly proper we then show that if the transfer function of the controller is strictly proper then the resulting closed loop system is asymptotically stable and if proper but not strictly proper then the resulting closed loop system is exponentially stable

co transduction for shape retrieval in this paper we propose a new shape object retrieval algorithm namely co transduction the performance of a retrieval system is critically decided by the accuracy of adopted similarity measures distances or metrics in shape object retrieval ideally intraclass objects should have smaller distances than interclass objects however it is a difficult task to design an ideal metric to account for the large intraclass variation different types of measures may focus on different aspects of the objects for example measures computed based on contours and skeletons are often complementary to each other our goal is to develop an algorithm to fuse different similarity measures for robust shape retrieval through a semisupervised learning framework we name our method co transduction which is inspired by the co training algorithm given two similarity measures and a query shape the algorithm iteratively retrieves the most similar shapes using one measure and assigns them to a pool for the other measure to do a re ranking and vice versa using co transduction we achieved an improved result of 97 72 bull s eye measure on the mpeg 7 data set over the state of the art performance we also present an algorithm called tri transduction to fuse multiple input similarities and it achieved 99 06 on the mpeg 7 data set our algorithm is general and it can be directly applied on input similarity measures metrics it is not limited to object shape retrieval and can be applied to other tasks for ranking retrieval

ensemble manifold regularization we propose an automatic approximation of the intrinsic manifold for general semi supervised learning ssl problems unfortunately it is not trivial to define an optimization function to obtain optimal hyperparameters usually cross validation is applied but it does not necessarily scale up other problems derive from the suboptimality incurred by discrete grid search and the overfitting therefore we develop an ensemble manifold regularization emr framework to approximate the intrinsic manifold by combining several initial guesses algorithmically we designed emr carefully so it 1 learns both the composite manifold and the semi supervised learner jointly 2 is fully automatic for learning the intrinsic manifold hyperparameters implicitly 3 is conditionally optimal for intrinsic manifold approximation under a mild and reasonable assumption and 4 is scalable for a large number of candidate manifold hyperparameters from both time and space perspectives furthermore we prove the convergence property of emr to the deterministic matrix at rate root n extensive experiments over both synthetic and real data sets demonstrate the effectiveness of the proposed framework

stabilization of an euler bernoulli beam equation via a corrupted boundary position feedback in this paper we are concerned with the stabilization of an euler bernoulli beam equation with a constant disturbance on the boundary observation a dynamic boundary controller is designed by using only the displacement measurement we obtain that the resulting closed loop system is asymptotically stable meanwhile the estimated function is shown to be convergent to the unknown disturbance as time goes to infinite

object detection with dog scale space a multiple kernel learning approach difference of gaussians dog scale space for an image is a significant way to generate features for object detection and classification while applying dog scale space features for object detection classification we face two inevitable issues dealing with high dimensional data and selecting weighting of proper scales the scale selection process is mostly ad hoc to present in this paper we propose a multiple kernel learning mkl method for both dog scale selection weighting and dealing with high dimensional scale space data we design a novel shift invariant kernel function for dog scale space to select only the useful scales in the dog scale space a novel framework of mkl is also proposed we utilize a 1 norm support vector machine svm in the mkl optimization problem for sparse weighting of scales from dog scale space the optimized data dependent kernel accommodates only a few scales that are most discriminatory according to the large margin principle with a 2 norm svm this learned kernel is applied to a challenging detection problem in oil sand mining to detect large lumps in oil sand videos we tested our method on several challenging oil sand data sets our method yields encouraging results on these difficult to process images and compares favorably against other popular multiple kernel methods

orientation and stabilization of a flexible beam attached to a rigid body planar motion the author considers a flexible structure modeled as a rigid body which rotates in inertial space a light flexible beam is clamped to the rigid body at one end and free one is clamped at the other it is assumed that the flexible beam performs only planar motion the equations of motion are obtained by using free body diagrams two control problems are posed namely the orientation and stabilization of the system it is shown that suitable boundary controls applied to the free end of the beam and suitable control torques applied to the rigid body solve the problems posed above the proofs are obtained by using the energy of the system as a lyapunov functional

scale affine invariant interest point detectors in this paper we propose a novel approach for detecting interest points invariant to scale and affine transformations our scale and affine invariant detectors are based on the following recent results 1 interest points extracted with the harris detector can be adapted to affine transformations and give repeatable results geometrically stable 2 the characteristic scale of a local structure is indicated by a local extremum over scale of normalized derivatives the laplacian 3 the affine shape of a point neighborhood is estimated based on the second moment matrix our scale invariant detector computes a multi scale representation for the harris interest point detector and then selects points at which a local measure the laplacian is maximal over scales this provides a set of distinctive points which are invariant to scale rotation and translation as well as robust to illumination changes and limited changes of viewpoint the characteristic scale determines a scale invariant region for each point we extend the scale invariant detector to affine invariance by estimating the affine shape of a point neighborhood an iterative algorithm modifies location scale and neighborhood of each point and converges to affine invariant points this method can deal with significant affine transformations including large scale changes the characteristic scale and the affine shape of neighborhood determine an affine invariant region for each point we present a comparative evaluation of different detectors and show that our approach provides better results than existing methods the performance of our detector is also confirmed by excellent matching resultss the image is described by a set of scale affine invariant descriptors computed on the regions associated with our points

robust boundary control of an axially moving string by using a pr transfer function in this note a scheme for the vibration suppression of a translating string using a positive real pr transfer function is investigated the transverse vibration of the string is controlled by hydraulic touch rolls located at the right end of the string the mathematical model of the system which consists of a hyperbolic partial differential equation pde describing the dynamics of the moving string and an ordinary differential equation ode for the actuator dynamics is derived by using hamilton s principle for translating continua the transfer function of the proposed boundary controller is a nonproper but pr function the asymptotic stability of the closed loop system in the presence of output disturbance is proved

shape classification using the inner distance part structure and articulation are of fundamental importance in computer and human vision we propose using the inner distance to build shape descriptors that are robust to articulation and capture part structure the inner distance is defined as the length of the shortest path between landmark points within the shape silhouette we show that it is articulation insensitive and more effective at capturing part structures than the euclidean distance this suggests that the inner distance can be used as a replacement for the euclidean distance to build more accurate descriptors for complex shapes especially for those with articulated parts in addition texture information along the shortest path can be used to further improve shape classification with this idea we propose three approaches to using the inner distance the first method combines the inner distance and multidimensional scaling mds to build articulation invariant signatures for articulated shapes the second method uses the inner distance to build a new shape descriptor based on shape contexts the third one extends the second one by considering the texture information along shortest paths the proposed approaches have been tested on a variety of shape databases including an articulated shape data set mpeg7 ce shape 1 kimia silhouettes the eth 80 data set two leaf data sets and a human motion silhouette data set in all the experiments our methods demonstrate effective performance compared with other algorithms

entire functions on c0 abstract it is shown that every holomorphic function on c 0 which is bounded on weakly compact sets is bounded on bounded sets

manifold regularized multitask learning for semi supervised multilabel image classification it is a significant challenge to classify images with multiple labels by using only a small number of labeled samples one option is to learn a binary classifier for each label and use manifold regularization to improve the classification performance by exploring the underlying geometric structure of the data distribution however such an approach does not perform well in practice when images from multiple concepts are represented by high dimensional visual features thus manifold regularization is insufficient to control the model complexity in this paper we propose a manifold regularized multitask learning mrmtl algorithm mrmtl learns a discriminative subspace shared by multiple classification tasks by exploiting the common structure of these tasks it effectively controls the model complexity because different tasks limit one another s search volume and the manifold regularization ensures that the functions in the shared hypothesis space are smooth along the data manifold we conduct extensive experiments on the pascal voc 07 dataset with 20 classes and the mir dataset with 38 classes by comparing mrmtl with popular image classification algorithms the results suggest that mrmtl is effective for image classification

nenmf an optimal gradient method for nonnegative matrix factorization nonnegative matrix factorization nmf is a powerful matrix decomposition technique that approximates a nonnegative matrix by the product of two low rank nonnegative matrix factors it has been widely applied to signal processing computer vision and data mining traditional nmf solvers include the multiplicative update rule mur the projected gradient method pg the projected nonnegative least squares pnls and the active set method as however they suffer from one or some of the following three problems slow convergence rate numerical instability and nonconvergence in this paper we present a new efficient nenmf solver to simultaneously overcome the aforementioned problems it applies nesterov s optimal gradient method to alternatively optimize one factor with another fixed in particular at each iteration round the matrix factor is updated by using the pg method performed on a smartly chosen search point where the step size is determined by the lipschitz constant since nenmf does not use the time consuming line search and converges optimally at rate in optimizing each matrix factor it is superior to mur and pg in terms of efficiency as well as approximation accuracy compared to pnls and as that suffer from numerical instability problem in the worst case nenmf overcomes this deficiency in addition nenmf can be used to solve norm norm and manifold regularized nmf with the optimal convergence rate numerical experiments on both synthetic and real world datasets show the efficiency of nenmf for nmf and its variants comparing to representative nmf solvers extensive experiments on document clustering suggest the effectiveness of nenmf

weakly continuous mappings on banach spaces abstract it is shown that every n homogeneous continuous polynomial on a banach space e which is weakly continuous on the unit ball of e is weakly uniformly continuous on the unit ball of e applications of the result to spaces of polynomials and holomorphic mappings on e are given

exponential stabilization of conservation systems with interior disturbance abstract in this paper we consider the stabilization problem of the conservation systems with interior disturbance employing the idea of sliding mode control we design a nonlinear distributed feedback controller we prove the solvability of the resulted closed loop system by the maximal monotone operator theory further we prove the exponential stability of the closed loop system in particular we prove that the requirement of a classical solution in the lyapunov approach is unnecessary

exact controllability stabilization and perturbations for distributed systems exact controllability is studied for distributed systems of hyperbolic type or for petrowsky systems like plate equations the control is a boundary control or a local distributed control exact controllability consists in trying to drive the system to rest in a given finite time the solution of the problems depends on the function spaces where the initial data are taken and also depends on the function space where the control can be chosen a systematic method named hum for hilbert uniqueness method is introduced as the terminology indicates it is based on uniqueness results classical or new and on hilbert spaces constructed in infinitely many ways by using uniqueness a number of applications are indicated having a general method for exact controllability implies having a general method for stabilization this leads to new and complicated nonlinear riccati s type pdes to be compared with direct methods when available in the last part of the paper we consider how all this behaves fo

a boundary control for motion synchronization of a two manipulator system with a flexible beam a novel synchronization motion control method is proposed in this paper for the system in which two manipulators are constrained by a flexible beam different from the general synchronization control method the coupling dynamics among various actuators is considered as the shear force which results from the synchronization errors then a simple boundary control is introduced to realize the synchronization motion of actuators by suppressing the shear force in order to avoid the drawbacks of assumed modes model the dynamic model of flexible beam is described by a distributed parameter model in this paper a riesz basis method is used to prove that the proposed control law can guarantee the synchronization system to be exponential stability simulation results demonstrate that the proposed method can effectively improve the performance of synchronization motion compared with other methods

accurate image search using the contextual dissimilarity measure this paper introduces the contextual dissimilarity measure which significantly improves the accuracy of bag of features based image search our measure takes into account the local distribution of the vectors and iteratively estimates distance update terms in the spirit of sinkhorn s scaling algorithm thereby modifying the neighborhood structure experimental results show that our approach gives significantly better results than a standard distance and outperforms the state of the art in terms of accuracy on the nisteacuter steweacutenius and lola data sets this paper also evaluates the impact of a large number of parameters including the number of descriptors the clustering method the visual vocabulary size and the distance measure the optimal parameter choice is shown to be quite context dependent in particular using a large number of descriptors is interesting only when using our dissimilarity measure we have also evaluated two novel variants multiple assignment and rank aggregation they are shown to further improve accuracy at the cost of higher memory usage and lower efficiency

a visual shape descriptor using sectors and shape context of contour lines this paper describes a visual shape descriptor based on the sectors and shape context of contour lines to represent the image local features used for image matching the proposed descriptor consists of two component feature vectors first the local region is separated into sectors and their gradient magnitude and orientation values are extracted a feature vector is then constructed from these values second local shape features are obtained using the shape context of contour lines another feature vector is then constructed from these contour lines the proposed approach calculates the local shape feature without needing to consider the edges this can overcome the difficulty associated with textured images and images with ill defined edges the combination of two component feature vectors makes the proposed descriptor more robust to image scale changes illumination variations and noise the proposed visual shape descriptor outperformed other descriptors in terms of the matching accuracy 14 525 better than sift 21 better than pca sift 11 86 better than gloh and 25 66 better than the shape context

decay of solutions of wave equations in a bounded region with boundary dissipation abstract an energy decay rate is obtained for solutions of wave type equations in a bounded region in rn whose boundary consists partly of a nontrapping reflecting surface and partly of an energy absorbing surface

holomorphic mappings on we describe the holomorphic mappings of bounded type and the arbitrary holomorphic mappings from the complex banach space 11 into a complex banach space x it is shown that these mappings have monomial expansions and the growth of the norms of the coefficients is characterized in each case this characterization is used to give new descriptions of the compact open topology and the nachbin ported topology on the space a 11 x of holomorphic mappings and to prove a lifting property for holomorphic mappings on 11 we also show that the monomials form an equicontinuous unconditional schauder basis for the space 11 so of holomorphic functions on 11 with the topology of uniform convergence on compact sets

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

geometric mean for subspace selection subspace selection approaches are powerful tools in pattern classification and data visualization one of the most important subspace approaches is the linear dimensionality reduction step in the fisher s linear discriminant analysis flda which has been successfully employed in many fields such as biometrics bioinformatics and multimedia information management however the linear dimensionality reduction step in flda has a critical drawback for a classification task with c classes if the dimension of the projected subspace is strictly lower than c 1 the projection to a subspace tends to merge those classes which are close together in the original feature space if separate classes are sampled from gaussian distributions all with identical covariance matrices then the linear dimensionality reduction step in flda maximizes the mean value of the kullback leibler kl divergences between different classes based on this viewpoint the geometric mean for subspace selection is studied in this paper three criteria are analyzed 1 maximization of the geometric mean of the kl divergences 2 maximization of the geometric mean of the normalized kl divergences and 3 the combination of 1 and 2 preliminary experimental results based on synthetic data uci machine learning repository and handwriting digits show that the third criterion is a potential discriminative subspace selection method which significantly reduces the class separation problem in comparing with the linear dimensionality reduction step in flda and its several representative extensions

a minimal state approach to dynamic stabilization of the rotating disk beam system with infinite memory this note is dedicated to the investigation of the stabilization problem of the well know rotating disk beam system we suggest a linear feedback law which consists of a torque control applied on the disk and a dynamic force control with infinite memory term exerted on the beam thereafter sufficient conditions on the angular velocity of the disk and the memory kernel are derived to assure the existence and uniqueness of solutions of the closed loop system the main ingredient of the proof of such a well posedness result is to adopt the method used for viscoelastic systems which mainly consists in utilizing the concept of minimal state variables moreover it is shown that the beam vibrations are forced to exponentially decay to zero while the disk keeps rotating with a fixed angular velocity this desirable property which can be interpreted as the exponential stability of the system is achieved by means of lyapunov energy method

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

robust symbolic representation for shape recognition and retrieval a new method for shape recognition and retrieval is proposed here the suggested algorithm is based on several steps the algorithm analyzes the contour of pairs of shapes their contours are recovered and represented by a pair of n points obtained by linear interpolation given two points p i and q j from the two shapes the cost of their matching is evaluated by using the shape context and by using dynamic programming the best matching between the point sets is obtained dynamic programming not only recovers the best matching but also identifies occlusions i e points in the two shapes which cannot be properly matched given the correspondence between the two point sets the two contours are aligned using procrustes analysis after alignment each contour is transformed into a string of symbols and a modified version of edit distance is used to compute the similarity between strings of symbols finally recognition and retrieval are obtained by a simple nearest neighbor procedure the algorithm has been tested on a large set of shape databases kimia mpeg 7 natural silhouette database gesture database marine database swedish leaf database diatom database eth 80 3d object database providing performances for both in recognition and in retrieval superior to most of previously proposed approaches

fick s law assisted propagation for semisupervised learning how to propagate the label information from labeled examples to unlabeled examples is a critical problem for graph based semisupervised learning many label propagation algorithms have been developed in recent years and have obtained promising performance on various applications however the eigenvalues of iteration matrices in these algorithms are usually distributed irregularly which slow down the convergence rate and impair the learning performance this paper proposes a novel label propagation method called fick s law assisted propagation flap unlike the existing algorithms that are directly derived from statistical learning flap is deduced on the basis of the theory of fick s first law of diffusion which is widely known as the fundamental theory in fluid spreading we prove that flap will converge with linear rate and show that flap makes eigenvalues of the iteration matrix distributed regularly comprehensive experimental evaluations on synthetic and practical datasets reveal that flap obtains encouraging results in terms of both accuracy and efficiency

factorization of holomorphic mappings in infinite dimensions this note is concerned with a generalization of the first identity to the non linear setting of holomorphic i e complex analytic functions on e in fact our interest in this generalization arose from a study made by the second author 15 of a suitable generalization of the second identity to the holomorphic setting however our results and methods have little in common with 15 in somewhat simplistic terms our results provide further evidence that in many situations one is justified in regarding the space h e of holomorphic functions on e as the holomorphic dual of e which shares many properties with the standard linear dual of e see for example 6 a brief outline of this paper follows after which we recall some basic results about holomorphic mappings between infinite dimensional locally convex spaces in section 2 we investigate when a holomorphic function f u c on a balanced open subset u of e admits a factorization through u a characterization of factorizable functions is given in terms of the derivative o f f our basic technique is

dynamic boundary control of a euler bernoulli beam a flexible beam clamped to a rigid base at one end and free at the other end is considered to stabilize the beam vibrations a dynamic boundary force control and a dynamic boundary torque control applied at the free end of the beam are proposed it is proved that with the proposed controls the beam vibrations decay exponentially the proof uses a lyapunov functional based on the energy functional of the system

on a class of marginally stable positive real systems a class of marginally stable positive real systems is defined which is less restrictive than the previously published definitions of strictly positive real systems a minimal realization and state space characterization of such systems are presented and it is proven that controllers belonging to this class robustly stabilize positive real plants

internal model based tracking and disturbance rejection for stable well posed systems in this paper we solve the tracking and disturbance rejection problem for infinite dimensional linear systems with reference and disturbance signals that are finite superpositions of sinusoids we explore two approaches both based on the internal model principle in the first approach we use a low gain controller and here our results are a partial extension of results by hamalainen and pohjolainen in their papers the plant is required to have an exponentially stable transfer function in the callier desoer algebra while in this paper we only require the plant to be well posed and exponentially stable these conditions are sufficiently unrestrictive to be verifiable for many partial differential equations in more than one space variable our second approach concerns the case when the second component of the plant transfer function from control input to tracking error is positive in this case we identify a very simple stabilizing controller which is again an internal model but which does not require low gain we apply our results to two problems involving systems modeled by partial differential equations the problem of rejecting external noise in a model for structure acoustics interactions and a similar problem for two coupled beams

learning context sensitive shape similarity by graph transduction shape similarity and shape retrieval are very important topics in computer vision the recent progress in this domain has been mostly driven by designing smart shape descriptors for providing better similarity measure between pairs of shapes in this paper we provide a new perspective to this problem by considering the existing shapes as a group and study their similarity measures to the query shape in a graph structure our method is general and can be built on top of any existing shape similarity measure for a given similarity measure a new similarity is learned through graph transduction the new similarity is learned iteratively so that the neighbors of a given shape influence its final similarity to the query the basic idea here is related to pagerank ranking which forms a foundation of google web search the presented experimental results demonstrate that the proposed approach yields significant improvements over the state of art shape matching algorithms we obtained a retrieval rate of 91 61 percent on the mpeg 7 data set which is the highest ever reported in the literature moreover the learned similarity by the proposed method also achieves promising improvements on both shape classification and shape clustering

technical communique an exponential stability result for the wave equation we consider a system described by the one dimensional linear wave equation in a bounded domain with appropriate boundary conditions to stabilize this system we propose a dynamic boundary controller applied at the free end of the system the transfer function of the proposed controller is a proper rational function which consists of a strictly positive real function and some poles on the imaginary axis we then show that under some conditions the closed loop system is exponentially stable

learning the kernel matrix with semidefinite programming kernel based learning algorithms work by embedding the data into a euclidean space and then searching for linear relations among the embedded data points the embedding is performed implicitly by specifying the inner products between each pair of points in the embedding space this information is contained in the so called kernel matrix a symmetric and positive semidefinite matrix that encodes the relative positions of all points specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space classical model selection problems in machine learning in this paper we show how the kernel matrix can be learned from data via semidefinite programming sdp techniques when applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm using the labeled part of the data one can learn an embedding also for the unlabeled part the similarity between test points is inferred from training points and their labels importantly these learning problems are convex so we obtain a method for learning both the model class and the function without local minima furthermore this approach leads directly to a convex method for learning the 2 norm soft margin parameter in support vector machines solving an important open problem

modeling stabilization and control of serially connected beams many flexible structures consist of a large number of components coupled end to end in the form of a chain in this paper we consider the simplest type of such structures which is formed by n seri

non negative patch alignment framework in this paper we present a non negative patch alignment framework npaf to unify popular non negative matrix factorization nmf related dimension reduction algorithms it offers a new viewpoint to better understand the common property of different nmf algorithms although multiplicative update rule mur can solve npaf and is easy to implement it converges slowly thus we propose a fast gradient descent fgd to overcome the aforementioned problem fgd uses the newton method to search the optimal step size and thus converges faster than mur experiments on synthetic and real world datasets confirm the efficiency of fgd compared with mur for optimizing npaf based on npaf we develop non negative discriminative locality alignment ndla experiments on face image and handwritten datasets suggest the effectiveness of ndla in classification tasks and its robustness to image occlusions compared with representative nmf related dimension reduction algorithms

affinity learning with diffusion on tensor product graph in many applications we are given a finite set of data points sampled from a data manifold and represented as a graph with edge weights determined by pairwise similarities of the samples often the pairwise similarities which are also called affinities are unreliable due to noise or due to intrinsic difficulties in estimating similarity values of the samples as observed in several recent approaches more reliable similarities can be obtained if the original similarities are diffused in the context of other data points where the context of each point is a set of points most similar to it compared to the existing methods our approach differs in two main aspects first instead of diffusing the similarity information on the original graph we propose to utilize the tensor product graph tpg obtained by the tensor product of the original graph with itself since tpg takes into account higher order information it is not a surprise that we obtain more reliable similarities however it comes at the price of higher order computational complexity and storage requirement the key contribution of the proposed approach is that the information propagation on tpg can be computed with the same computational complexity and the same amount of storage as the propagation on the original graph we prove that a graph diffusion process on tpg is equivalent to a novel iterative algorithm on the original graph which is guaranteed to converge after its convergence we obtain new edge weights that can be interpreted as new learned affinities we stress that the affinities are learned in an unsupervised setting we illustrate the benefits of the proposed approach for data manifolds composed of shapes images and image patches on two very different tasks of image retrieval and image segmentation with learned affinities we achieve the bull s eye retrieval score of 99 99 percent on the mpeg 7 shape dataset which is much higher than the state of the art algorithms when the data points are image patches the ncut with the learned affinities not only significantly outperforms the ncut with the original affinities but it also outperforms state of the art image segmentation methods

stability of an euler bernoulli beam with a nonlinear dynamic feedback system this paper is concerned with the stability analysis of a lossless euler bernoulli beam that carries a tip payload which is coupled to a finite dimensional nonlinear dynamic feedback system the latter comprises dynamic systems satisfying the nonlinear kyp lemma which may represent the closed loop dynamics of subordinate controlled actuators as well as the interaction with a nonlinear passive environment global in time wellposedness and asymptotic stability is rigorously proven for the resulting closed loop partial differential equation ordinary differential equation pde ode system the analysis is based on semigroup theory for the corresponding first order evolution problem for the large time analysis precompactness of the trajectories is shown by deriving uniform in time bounds on the solution and its time derivatives

lyapunov based boundary control for a class of hyperbolic lotka volterra systems this paper considers a boundary feedback control problem for two first order nonlinearly coupled hyperbolic partial differential equations with lotka volterra type coupling boundary control action is used on one equation to drive the state at the end of the spatial domain to a desired constant reference value static and dynamic boundary controllers are designed based on a special lyapunov functional that is related to an entropy function the time derivative of the entropy function is made strictly negative by an appropriate choice of boundary conditions a unique classical solution is shown to exist globally in time and asymptotic exponential convergence to the desired steady state solution is shown in the norm the boundary control design is illustrated with simulations

multiple kernel learning algorithms in recent years several methods have been proposed to combine multiple kernels instead of using a single one these different kernels may correspond to using different notions of similarity or may be using information coming from multiple sources different representations or different feature subsets in trying to organize and highlight the similarities and differences between them we give a taxonomy of and review several multiple kernel learning algorithms we perform experiments on real data sets for better illustration and comparison of existing algorithms we see that though there may not be large differences in terms of accuracy there is difference between them in complexity as given by the number of stored support vectors the sparsity of the solution as given by the number of used kernels and training time complexity we see that overall using multiple kernels instead of a single one is useful and believe that combining kernels in a nonlinear or data dependent way seems more promising than linear combination in fusing information provided by simple linear kernels whereas linear methods are more reasonable when combining complex gaussian kernels