graph transduction as a noncooperative game graph transduction is a popular class of semisupervised learning techniques that aims to estimate a classification function defined over a graph of labeled and unlabeled data points the general idea is to propagate the provided label information to unlabeled nodes in a consistent way in contrast to the traditional view in which the process of label propagation is defined as a graph laplacian regularization this article proposes a radically different perspective based on game theoretic notions within the proposed framework the transduction problem is formulated in terms of a noncooperative multiplayer game whereby equilibria correspond to consistent labelings of the data an attractive feature of this formulation is that it is inherently a multiclass approach and imposes no constraint whatsoever on the structure of the pairwise similarity matrix being able to naturally deal with asymmetric and negative similarities alike experiments on a number of real world problems demonstrate that the proposed approach performs well compared with state of the art algorithms and it can deal effectively with various types of similarity relations

learning discriminative subspaces on random contrasts for image saliency analysis in visual saliency estimation one of the most challenging tasks is to distinguish targets and distractors that share certain visual attributes with the observation that such targets and distractors can sometimes be easily separated when projected to specific subspaces we propose to estimate image saliency by learning a set of discriminative subspaces that perform the best in popping out targets and suppressing distractors toward this end we first conduct principal component analysis on massive randomly selected image patches the principal components which correspond to the largest eigenvalues are selected to construct candidate subspaces since they often demonstrate impressive abilities to separate targets and distractors by projecting images onto various subspaces we further characterize each image patch by its contrasts against randomly selected neighboring and peripheral regions in this manner the probable targets often have the highest responses while the responses at background regions become very low based on such random contrasts an optimization framework with pairwise binary terms is adopted to learn the saliency model that best separates salient targets and distractors by optimally integrating the cues from various subspaces experimental results on two public benchmarks show that the proposed approach outperforms 16 state of the art methods in human fixation prediction

cross domain object recognition via input output kernel analysis it is of great importance to investigate the domain adaptation problem of image object recognition because now image data is available from a variety of source domains to understand the changes in data distributions across domains we study both the input and output kernel spaces for cross domain learning situations where most labeled training images are from a source domain and testing images are from a different target domain to address the feature distribution change issue in the reproducing kernel hilbert space induced by vector valued functions we propose a domain adaptive input output kernel learning da iokl algorithm which simultaneously learns both the input and output kernels with a discriminative vector valued decision function by reducing the data mismatch and minimizing the structural error we also extend the proposed method to the cases of having multiple source domains we examine two cross domain object recognition benchmark data sets and the proposed method consistently outperforms the state of the art domain adaptation and multiple kernel learning methods

training free generic object detection using locally adaptive regression kernels we present a generic detection localization algorithm capable of searching for a visual object of interest without training the proposed method operates using a single example of an object of interest to find similar matches does not require prior knowledge learning about objects being sought and does not require any preprocessing step or segmentation of a target image our method is based on the computation of local regression kernels as descriptors from a query which measure the likeness of a pixel to its surroundings salient features are extracted from said descriptors and compared against analogous features from the target image this comparison is done using a matrix generalization of the cosine similarity measure we illustrate optimality properties of the algorithm using a naive bayes framework the algorithm yields a scalar resemblance map indicating the likelihood of similarity between the query and all patches in the target image by employing nonparametric significance tests and nonmaxima suppression we detect the presence and location of objects similar to the given query the approach is extended to account for large variations in scale and rotation high performance is demonstrated on several challenging data sets indicating successful detection of objects in diverse contexts and under different imaging conditions

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

feature combination and the knn framework in object classification in object classification feature combination can usually be used to combine the strength of multiple complementary features and produce better classification results than any single one while multiple kernel learning mkl is a popular approach to feature combination in object classification it does not always perform well in practical applications on one hand the optimization process in mkl usually involves a huge consumption of computation and memory space on the other hand in some cases mkl is found to perform no better than the baseline combination methods this observation motivates us to investigate the underlying mechanism of feature combination with average combination and weighted average combination as a result we empirically find that in average combination it is better to use a sample of the most powerful features instead of all whereas in one type of weighted average combination the best classification accuracy comes from a nearly sparse combination we integrate these observations into the k nearest neighbors knns framework based on which we further discuss some issues related to sparse solution and mkl finally by making use of the knn framework we present a new weighted average combination method which is shown to perform better than mkl in both accuracy and efficiency in experiments we believe that the work in this paper is helpful in exploring the mechanism underlying feature combination

a least squares method for finding the preisach hysteresis operator from measurements a finite element like least squares method is introduced for determining the density function in the preisach hysteresis model from overdeterined measured data it is shown that the least squares error depends on the quality of the data and the best approximations to the analytic density for consistent data criteria are given for convergence of the approximate density and preisach operator with increasing measurements

multilinear supervised neighborhood embedding of a local descriptor tensor for scene object recognition in this paper we propose to represent an image as a local descriptor tensor and use a multilinear supervised neighborhood embedding msne for discriminant feature extraction which is able to be used for subject or scene recognition the contributions of this paper include 1 a novel feature extraction approach denoted as the histogram of orientation weighted with a normalized gradient nhog for local region representation which is robust to large illumination variation in an image 2 an image representation framework denoted as the local descriptor tensor which can effectively combine a moderate amount of local features together for image representation and be more efficient than the popular existing bag of feature model and 3 an msne analysis algorithm which can directly deal with the local descriptor tensor for extracting discriminant and compact features and at the same time preserve neighborhood structure in tensor feature space for subject scene recognition we demonstrate the performance advantages of our proposed approach over existing techniques on different types of benchmark database such as a scene data set i e ot8 face data sets i e yale and pie and view based object data sets coil 100 and eth 80

texture characterization using shape co occurrence patterns texture characterization is a key problem in image understanding and pattern recognition in this paper we present a flexible shape based texture representation using shape co occurrence patterns more precisely texture images are first represented by a tree of shapes each of which is associated with several geometrical and radiometric attributes then four typical kinds of shape co occurrence patterns based on the hierarchical relationships among the shapes in the tree are learned as codewords three different coding methods are investigated for learning the codewords which can be used to encode any given texture image into a descriptive vector in contrast with existing works the proposed approach not only inherits the shape based method s strong ability to capture geometrical aspects of textures and high robustness to variations in imaging conditions but also provides a flexible way to consider shape relationships and to compute high order statistics on the tree to the best of our knowledge this is the first time that co occurrence patterns of explicit shapes have been used as a tool for texture analysis experiments on various texture and scene data sets demonstrate the efficiency of the proposed approach

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

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

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

image segmentation via probabilistic graph matching this paper presents an unsupervised and semi automatic image segmentation approach where we formulate the segmentation as an inference problem based on unary and pairwise assignment probabilities computed using low level image cues the inference is solved via a probabilistic graph matching scheme which allows rigorous incorporation of low level image cues and automatic tuning of parameters the proposed scheme is experimentally shown to compare favorably with contemporary semi supervised and unsupervised image segmentation schemes when applied to contemporary state of the art image sets

global hashing system for fast image search hashing methods have been widely investigated for fast approximate nearest neighbor searching in large data sets most existing methods use binary vectors in lower dimensional spaces to represent data points that are usually real vectors of higher dimensionality we divide the hashing process into two steps data points are first embedded in a low dimensional space and the global positioning system method is subsequently introduced but modified for binary embedding we devise dataindependent and data dependent methods to distribute the satellites at appropriate locations our methods are based on finding the tradeoff between the information losses in these two steps experiments show that our data dependent method outperforms other methods in different sized data sets from 100k to 10m by incorporating the orthogonality of the code matrix both our data independent and data dependent methods are particularly impressive in experiments on longer bits

entropy rate clustering cluster analysis via maximizing a submodular function subject to a matroid constraint we propose a new objective function for clustering this objective function consists of two components the entropy rate of a random walk on a graph and a balancing term the entropy rate favors formation of compact and homogeneous clusters while the balancing function encourages clusters with similar sizes and penalizes larger clusters that aggressively group samples we present a novel graph construction for the graph associated with the data and show that this construction induces a matroid a combinatorial structure that generalizes the concept of linear independence in vector spaces the clustering result is given by the graph topology that maximizes the objective function under the matroid constraint by exploiting the submodular and monotonic properties of the objective function we develop an efficient greedy algorithm furthermore we prove an approximation bound of 1 2 for the optimality of the greedy solution we validate the proposed algorithm on various benchmarks and show its competitive performances with respect to popular clustering algorithms we further apply it for the task of superpixel segmentation experiments on the berkeley segmentation data set reveal its superior performances over the state of the art superpixel segmentation algorithms in all the standard evaluation metrics

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

the ordinal relation preserving binary codes hashing algorithm has been widely used for efficient approximate nearest neighbor ann search learning optimal hashing functions has been given focus and it is still a challenge this paper aims to effectively and efficiently generate relative similarity preserving binary codes most existing hashing methods try to preserve the locality similarity by preserving direct distance similarity while ignoring the relative similarity which advantages in ann search in this paper this issue is solved by proposing the relative error which emphasizes that the ordinal relations in hamming space and euclidean space should be consistent with each other we learn hashing projection functions via two steps the first step adopts the lookup based mechanism to find the optimal binary codes of training data which can preserve the relative similarity and simultaneously adapt to data distribution the binary codes in the first step are considered as supervision information in the second step the objective of the second step is to learn hashing projection functions which can efficiently regenerate the binary codes in the first step aim to be in accordance with the property of data distribution the hyper internal tangent planes of two specified spheres are chosen as hashing projection functions assisted by these projection functions the time complexity of encoding process is greatly reduced experimental results on four public data sets demonstrate that our method outperforms many other state of the art methods author highlightsan effective and efficient binary coding framework which enjoys the merits of lookup based and hamming based methods is proposed relative error is proposed to preserve the relative sensitivity the effective tangent planes are adopted as hashing functions to avoid computing the complex classification problem the encoding error guarantees that the selected hashing functions have good performance

convergence of direct recursive algorithm for identification of preisach hysteresis model with stochastic input we consider a recursive iterative algorithm for identification of parameters of the preisach model one of the most commonly used models of hysteretic input output relationships this online algorithm uses a simple rule for updating the values of the piecewise constant density function in the switching region at each time step the so called persistent excitation condition has been shown to play an important role for convergence of recursive iteration schemes when input output data are generated by a deterministic input such as for example a periodically repeated sequence of test inputs prescribed by the classical mayergoyz identification algorithm in this work we assume that the input randomly fluctuates and these fluctuations can be described by a stochastic markov process assuming that accurate measurements of the input and output are available we prove the exponential convergence of the recursive identification algorithm estimate explicitly the convergence rate and explore which properties of the stochastic input and the algorithm affect the guaranteed convergence rate an analogue of the persistent excitation condition suitable for analysis of stochastic markov inputs is established numerical examples that test the convergence of the algorithm in the case of a time dependent density function and in the presence of measurement noise are presented

hashing with angular reconstructive embeddings large scale search methods are increasingly critical for many content based visual analysis applications among which hashing based approximate nearest neighbor search techniques have attracted broad interests due to their high efficiency in storage and retrieval however existing hashing works are commonly designed for measuring data similarity by the euclidean distances in this paper we focus on the problem of learning compact binary codes using the cosine similarity specifically we proposed novel angular reconstructive embeddings are method which aims at learning binary codes by minimizing the reconstruction error between the cosine similarities computed by original features and the resulting binary embeddings furthermore we devise two efficient algorithms for optimizing our are in continuous and discrete manners respectively we extensively evaluate the proposed are on several large scale image benchmarks the results demonstrate that are outperforms several state of the art methods

motion blur identification in noisy images using mathematical models and statistical measures motion blur is one of the most common blurs that degrades images restoration of such images is highly dependent on estimation of motion blur parameters since 1976 many researchers have developed algorithms to estimate linear motion blur parameters these algorithms are different in their performance time complexity precision and robustness in noisy environments in this paper we have presented a novel algorithm to estimate linear motion blur parameters such as direction and length we used radon transform to find direction and bispectrum modeling to find the length of motion our algorithm is based on the combination of spatial and frequency domain analysis the great benefit of our algorithm is its robustness and precision in noisy images we used statistical measures to prove goodness of our model our method was tested on 80 standard images that were degraded with different directions and motion lengths with additive gaussian noise the error tolerance average of the estimated parameters was 0 9 o in direction and 0 95 pixel in length and the standard deviations were 0 69 and 0 85 respectively

one shot detection with laplacian object and fast matrix cosine similarity one shot generic object detection involves searching for a single query object in a larger target image relevant approaches have benefited from features that typically model the local similarity patterns in this paper we combine local similarity encoded by local descriptors with a global context i e a graph structure of pairwise affinities among the local descriptors embedding the query descriptors into a low dimensional but discriminatory subspace unlike principal components that preserve global structure of feature space we actually seek a linear approximation to the laplacian eigenmap that permits us a locality preserving embedding of high dimensional region descriptors our second contribution is an accelerated but exact computation of matrix cosine similarity as the decision rule for detection obviating the computationally expensive sliding window search we leverage the power of fourier transform combined with integral image to achieve superior runtime efficiency that allows us to test multiple hypotheses for pose estimation within a reasonably short time our approach to one shot detection is training free and experiments on the standard data sets confirm the efficacy of our model besides low computation cost of the proposed codebook free object detector facilitates rather straightforward query detection in large data sets including movie videos

properties of solutions to a class of differential models incorporating preisach hysteresis operator abstract we consider a class of equations which have been recently proposed as a mathematical tool for modelling dynamics of hydrological economic and biological systems exhibiting hysteresis and related memory effects the detail of the modelling approach is illustrated by an example from the hydrological context where a balance equation is coupled with a hysteretic constitutive relationship between the water content in the soil and the matric potential m of the soil matrix and where the preisach hysteresis operator is used as a model of this constitutive relationship in particular we present assumptions which eliminate spatial variation and lead to balance equations in the form of odes two examples of such hydrological models are considered followed by a less detailed discussion of applications of similar modelling approach and equations in economics and biology in the proposed formalism the closed system is described by an operator differential equation where the rate of change of the output of the preisach operator is a function of its input and time in the main part of the paper for such operator differential equations we study the initial value problem uniqueness existence extendability of solutions their dependence on initial data and the structure of the projection of the phase portrait onto the t m plane solutions are characterised by jumps of the derivative induced by either of the two reasons one is the memory of past extremum values of the solution the other is a singularity at the lines of zero flow we analyse the singularity and calculate the value of the jumps thus providing an important input to numerical solution of the equation furthermore we identify possible non uniqueness points and points of sensitivity to small perturbations of initial data as well as conditions that ensure uniqueness and stability to such perturbations regularisation of the equation and natural monotonicity conditions ensuring global stability of a periodic solution for the equation with periodic input are discussed rigorous analysis of the well posedness of the models is presented for the first time

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

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

an approach to the parameterization of structure for fast categorization a decomposition is described which parameterizes the geometry and appearance of contours and regions of gray scale images with the goal of fast categorization to express the contour geometry a contour is transformed into a local global space from which parameters are derived classifying its global geometry arc inflexion or alternating and describing its local aspects degree of curvature edginess symmetry regions are parameterized based on their symmetric axes which are evolved with a wave propagation process enabling to generate the distance map for fragmented contour images the methodology is evaluated on three image sets the caltech 101 set and two sets drawn from the corel collection the performance nearly reaches the one of other categorization systems for unsupervised learning

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

efficient circular thresholding otsu s algorithm for thresholding images is widely used and the computational complexity of determining the threshold from the histogram is o n where n is the number of histogram bins when the algorithm is adapted to circular rather than linear histograms then two thresholds are required for binary thresholding we show that surprisingly it is still possible to determine the optimal threshold in o n time the efficient optimal algorithm is over 300 times faster than traditional approaches for typical histograms and is thus particularly suitable for real time applications we further demonstrate the usefulness of circular thresholding using the adapted otsu criterion for various applications including analysis of optical flow data indoor outdoor image classification and non photorealistic rendering in particular by combining circular otsu feature with other colour texture features a 96 9 correct rate is obtained for indoor outdoor classification on the well known iitm scid2 data set outperforming the state of the art result by 4 3

generating fluttering patterns with low autocorrelation for coded exposure imaging the performance of coded exposure imaging critically depends on finding good binary sequences previous coded exposure imaging methods have mostly relied on random searching to find the binary codes but that approach can easily fail to find good long sequences due to the exponentially expanding search space in this paper we present two algorithms for generating the binary sequences which are especially well suited for generating short and long binary sequences respectively we show that the concept of low autocorrelation binary sequences which has been successfully exploited in the field of information theory can be applied to generate shutter fluttering patterns we also propose a new measure for good binary sequences based on the new measure we introduce two new algorithms for coded exposure imaging a modified legendre sequence method and a memetic algorithm experiments using both synthetic and real data show that our new algorithms consistently generate better binary sequences for the coded exposure problem yielding better deblurring and resolution enhancement results compared to previous methods of generating the binary codes

scalable similarity search with topology preserving hashing hashing based similarity search techniques is becoming increasingly popular in large data sets to capture meaningful neighbors the topology of a data set which represents the neighborhood relationships between its subregions and the relative proximities between the neighbors of each subregion e g the relative neighborhood ranking of each subregion should be exploited however most existing hashing methods are developed to preserve neighborhood relationships while ignoring the relative neighborhood proximities moreover most hashing methods lack in providing a good result ranking since there are often lots of results sharing the same hamming distance to a query in this paper we propose a novel hashing method to solve these two issues jointly the proposed method is referred to as topology preserving hashing tph tph is distinct from prior works by also preserving the neighborhood ranking based on this framework we present three different tph methods including linear unsupervised tph semisupervised tph and kernelized tph particularly our unsupervised tph is capable of mining semantic relationship between unlabeled data without supervised information extensive experiments on four large data sets demonstrate the superior performances of the proposed methods over several state of the art unsupervised and semisupervised hashing techniques

joint image clustering and labeling by matrix factorization we propose a novel algorithm to cluster and annotate a set of input images jointly where the images are clustered into several discriminative groups and each group is identified with representative labels automatically for these purposes each input image is first represented by a distribution of candidate labels based on its similarity to images in a labeled reference image database a set of these label based representations are then refined collectively through a non negative matrix factorization with sparsity and orthogonality constraints the refined representations are employed to cluster and annotate the input images jointly the proposed approach demonstrates performance improvements in image clustering over existing techniques and illustrates competitive image labeling accuracy in both quantitative and qualitative evaluation in addition we extend our joint clustering and labeling framework to solving the weakly supervised image classification problem and obtain promising results

asymptotic stability of continuum sets of periodic solutions to systems with hysteresis abstract we consider hysteresis perturbations of a system of odes which possesses an asymptotically stable periodic solution z where the oscillation of an appropriate projection of this periodic solution is smaller than some threshold number defined by the hysteresis nonlinearity it is shown that the perturbed system has a continuum of periodic solutions with a rather simple structure in a vicinity of z the main result is a theorem on the stability of this continuum

multiple feature kernel hashing for large scale visual search recently hashing has become attractive in large scale visual search owing to its theoretical guarantee and practical success however most of the state of the art hashing methods can only employ a single feature type to learn hashing functions related research on image search clustering and other domains has proved the advantages of fusing multiple features in this paper we propose a novel multiple feature kernel hashing framework where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features the framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features but also general for both supervised and unsupervised scenarios we present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination experimental results on three large scale benchmarks cifar 10 nus wide and a trecvid show that the proposed approach can achieve superior accuracy and efficiency over state of the art methods highlightswe propose a generic multiple feature hashing framework using multiple kernels visual features are implicitly mapped and concatenated to reduce complexity we formulate both supervised and unsupervised hashing problems in the framework alternating optimization ways efficiently learn hashing functions and the kernel space experiments validate the superior performances and efficiency of the proposed approach

fast semi supervised clustering with enhanced spectral embedding in recent years semi supervised clustering ssc has aroused considerable interests from the machine learning and data mining communities in this paper we propose a novel ssc approach with enhanced spectral embedding ese which not only considers the geometric structure information contained in data sets but also can make use of the given side information such as pairwise constraints specially we first construct a symmetry favored k nn graph which is highly robust to noise and outliers and can reflect the underlying manifold structures of data sets then we learn the enhanced spectral embedding towards an ideal data representation as consistent with the given pairwise constraints as possible finally by using the regularization of spectral embedding we formulate learning the new data representation as a semidefinite quadratic linear programming sqlp problem which can be efficiently solved experimental results on a variety of synthetic and real world data sets show that our ese approach outperforms the state of the art ssc algorithms in terms of speed and quality on both vector based and graph based clustering

multiview alignment hashing for efficient image search hashing is a popular and efficient method for nearest neighbor search in large scale data spaces by embedding high dimensional feature descriptors into a similarity preserving hamming space with a low dimension for most hashing methods the performance of retrieval heavily depends on the choice of the high dimensional feature descriptor furthermore a single type of feature cannot be descriptive enough for different images when it is used for hashing thus how to combine multiple representations for learning effective hashing functions is an imminent task in this paper we present a novel unsupervised multiview alignment hashing approach based on regularized kernel nonnegative matrix factorization which can find a compact representation uncovering the hidden semantics and simultaneously respecting the joint probability distribution of data in particular we aim to seek a matrix factorization to effectively fuse the multiple information sources meanwhile discarding the feature redundancy since the raised problem is regarded as nonconvex and discrete our objective function is then optimized via an alternate way with relaxation and converges to a locally optimal solution after finding the low dimensional representation the hashing functions are finally obtained through multivariable logistic regression the proposed method is systematically evaluated on three data sets 1 caltech 256 2 cifar 10 and 3 cifar 20 and the results show that our method significantly outperforms the state of the art multiview hashing techniques

robust imagegraph rank level feature fusion for image search recently feature fusion has demonstrated its effectiveness in image search however bad features and inappropriate parameters usually bring about false positive images i e outliers leading to inferior performance therefore a major challenge of fusion scheme is how to be robust to outliers towards this goal this paper proposes a rank level framework for robust feature fusion first we define rank distance to measure the relevance of images at rank level based on it bayes similarity is introduced to evaluate the retrieval quality of individual features through which true matches tend to obtain higher weight than outliers then we construct the directed imagegraph to encode the relationship of images each image is connected to its k nearest neighbors with an edge and the edge is weighted by bayes similarity multiple rank lists resulted from different methods are merged via imagegraph furthermore on the fused imagegraph local ranking is performed to re order the initial rank lists it aims at local optimization and thus is more robust to global outliers extensive experiments on four benchmark data sets validate the effectiveness of our method besides the proposed method outperforms two popular fusion schemes and the results are competitive to the state of the art

wavelet domain multifractal analysis for static and dynamic texture classification in this paper we propose a new texture descriptor for both static and dynamic textures the new descriptor is built on the wavelet based spatial frequency analysis of two complementary wavelet pyramids standard multiscale and wavelet leader these wavelet pyramids essentially capture the local texture responses in multiple high pass channels in a multiscale and multiorientation fashion in which there exists a strong power law relationship for natural images such a power law relationship is characterized by the so called multifractal analysis in addition two more techniques scale normalization and multiorientation image averaging are introduced to further improve the robustness of the proposed descriptor combining these techniques the proposed descriptor enjoys both high discriminative power and robustness against many environmental changes we apply the descriptor for classifying both static and dynamic textures our method has demonstrated excellent performance in comparison with the state of the art approaches in several public benchmark datasets

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

design and estimation of coded exposure point spread functions we address the problem of motion deblurring using coded exposure this approach allows for accurate estimation of a sharp latent image via well posed deconvolution and avoids lost image content that cannot be recovered from images acquired with a traditional shutter previous work in this area has used either manual user input or alpha matting approaches to estimate the coded exposure point spread function psf from the captured image in order to automate deblurring and to avoid the limitations of matting approaches we propose a fourier domain statistical approach to coded exposure psf estimation that allows us to estimate the latent image in cases of constant velocity constant acceleration and harmonic motion we further demonstrate that previously used criteria to choose a coded exposure psf do not produce one with optimal reconstruction error and that an additional 30 percent reduction in root mean squared error rmse of the latent image estimate can be achieved by incorporating natural image statistics

data dependent hashing based on p stable distribution the p stable distribution is traditionally used for data independent hashing in this paper we describe how to perform data dependent hashing based on p stable distribution we commence by formulating the euclidean distance preserving property in terms of variance estimation based on this property we develop a projection method which maps the original data to arbitrary dimensional vectors each projection vector is a linear combination of multiple random vectors subject to p stable distribution in which the weights for the linear combination are learned based on the training data an orthogonal matrix is then learned data dependently for minimizing the thresholding error in quantization combining the projection method and orthogonal matrix we develop an unsupervised hashing scheme which preserves the euclidean distance compared with data independent hashing methods our method takes the data distribution into consideration and gives more accurate hashing results with compact hash codes different from many data dependent hashing methods our method accommodates multiple hash tables and is not restricted by the number of hash functions to extend our method to a supervised scenario we incorporate a supervised label propagation scheme into the proposed projection method this results in a supervised hashing scheme which preserves semantic similarity of data experimental results show that our methods have outperformed several state of the art hashing approaches in both effectiveness and efficiency

multiresolution gray scale and rotation invariant texture classification with local binary patterns presents a theoretically very simple yet efficient multiresolution approach to gray scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions the method is based on recognizing that certain local binary patterns termed uniform are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature we derive a generalized gray scale and rotation invariant operator presentation that allows for detecting the uniform patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis the proposed approach is very robust in terms of gray scale variations since the operator is by definition invariant against any monotonic transformation of the gray scale another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table experimental results demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns

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

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

stability of large cycles in a nonsmooth problem with hopf bifurcation at infinity an autonomous system which have a principal linear part at infinity and a continuous nonlinear term of sublinear growth is investigated results for systems with smooth nonlinearities are presented where accurate asymptotics of periodic cycles were introduced and cycle stability analyzed by the usual linearization of the system in a neighborhood of the cycle a new method to study both existence and stability of large cycles for systems with nonsmooth nonlinearities asymptotically homogeneous at infinity is developed the method combines the parameter functionalization technique and methods of monotone concave operators

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

feature selection for clustering using instance based learning by exploring the nearest and farthest neighbors feature selection for clustering is an active research topic and is used to identify salient features that are helpful for data clustering while partitioning a dataset into clusters a data instance and its nearest neighbors will belong to the same cluster and this instance and its farthest neighbors will belong to different clusters we propose a new feature selection method to identify salient features that are useful for maintaining the instance s nearest neighbors and farthest neighbors referred to here as fsnf in particular fsnf uses the mutual information criterion to estimate feature salience by considering maintainability experiments on benchmark datasets demonstrate the effectiveness of fsnf within the context of cluster analysis

multiple level feature based measure for retargeted image quality objective image retargeting quality assessment aims to use computational models to predict the retargeted image quality consistent with subjective perception in this paper we propose a multiple level feature mlf based quality measure to predict the perceptual quality of retargeted images we first provide an in depth analysis on the low level aspect ratio similarity feature and then propose a mid level edge group similarity feature to better address the shape structure related distortion furthermore a high level face block similarity feature is designed to deal with sensitive region deformation the multiple level features are complementary as they quantify different aspects of quality degradation in the retargeted image and the mlf measure learned by regression is used to predict the perceptual quality of retargeted images extensive experimental results performed on two public benchmark databases demonstrate that the proposed mlf measure achieves higher quality prediction accuracy than the existing relevant state of the art quality measures

scene oriented hierarchical classification of blurry and noisy images a system for scene oriented hierarchical classification of blurry and noisy images is proposed it attempts to simulate important features of the human visual perception the underlying approach is based on three strategies extraction of essential signatures captured from a global context simulating the global pathway highlight detection based on local conspicuous features of the reconstructed image simulating the local pathway and hierarchical classification of extracted features using probabilistic techniques the techniques involved in hierarchical classification use input from both the local and global pathways visual context is exploited by a combination of gabor filtering with the principal component analysis in parallel a pseudo restoration process is applied together with an affine invariant approach to improve the accuracy in the detection of local conspicuous features subsequently the local conspicuous features and the global essential signature are combined and clustered by a monte carlo approach finally clustered features are fed to a self organizing tree algorithm to generate the final hierarchical classification results selected representative results of a comprehensive experimental evaluation validate the proposed system

new fractional order legendre fourier moments for pattern recognition applications abstract orthogonal moments enable computer based systems to discriminate between similar objects mathematicians proved that the orthogonal polynomials of fractional orders outperformed their corresponding counterparts in representing the fine details of a given function in this work novel orthogonal fractional order legendre fourier moments are proposed for pattern recognition applications the basis functions of these moments are defined and the essential mathematical equations for the recurrence relations orthogonality and the similarity transformations rotation and scaling are derived the proposed new fractional order moments are tested where their performance is compared with the existing orthogonal quaternion multi channel and fractional moments new descriptors were found to be superior to the existing ones in terms of accuracy stability noise resistance invariance to similarity transformations recognition rates and computational times

explicit ringing removal in image deblurring in this paper we present a simple yet effective image deblurring method to produce ringing free deblurred images our work is inspired by the observation that large scale deblurring ringing artifacts are measurable through a multi resolution pyramid of low pass filtering of the blurred deblurred image pair we propose to model such a quantification as a convex cost function and minimize it directly in the deblurring process in order to reduce ringing regardless of its cause an efficient primal dual algorithm is proposed as a solution to this optimization problem since the regularization is more biased toward ringing patterns the details of the reconstructed image are prevented from over smoothing an inevitable source of ringing is sensor saturation which can be detected costlessly contrary to most other sources of ringing however dealing with the saturation effect in deblurring introduces a non linear operator in optimization problem in this paper we also introduce a linear approximation as a solution to handling saturation in the proposed deblurring method as a result of these steps we significantly enhance the quality of the deblurred images experimental results and quantitative evaluations demonstrate that the proposed method performs favorably against state of the art image deblurring methods

improving bag of features for large scale image search this article improves recent methods for large scale image search we first analyze the bag of features approach in the framework of approximate nearest neighbor search this leads us to derive a more precise representation based on hamming embedding he and weak geometric consistency constraints wgc he provides binary signatures that refine the matching based on visual words wgc filters matching descriptors that are not consistent in terms of angle and scale he and wgc are integrated within an inverted file and are efficiently exploited for all images in the dataset we then introduce a graph structured quantizer which significantly speeds up the assignment of the descriptors to visual words a comparison with the state of the art shows the interest of our approach when high accuracy is needed experiments performed on three reference datasets and a dataset of one million of images show a significant improvement due to the binary signature and the weak geometric consistency constraints as well as their efficiency estimation of the full geometric transformation i e a re ranking step on a short list of images is shown to be complementary to our weak geometric consistency constraints our approach is shown to outperform the state of the art on the three datasets

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

online primal dual learning for a data dependent multi kernel combination model with multiclass visual categorization applications the task of visual categorization becomes very challenging as the number of samples or classes increases this is mainly due to large memory requirements or a crowded semantic space which causes class separation difficulties in this paper a new approach called oprdum2 online primal dual multiple kernel multiple class is proposed this new algorithm can significantly reduce the complexity of visual categorization under this approach an elegant heterogeneous feature fusion machine is used to consolidate complementary class discriminative information and online learning is applied to deal with large sample sizes to be specific a data dependent multi kernel machine that fuses multiple heterogeneous features in a nonlinear manner is defined by extending the multi kernel learning model and multivariate hinge loss and conservative updating rules are used to increase the sample level sparsity in the model given the intrinsic hierarchical structure of samples features and classes an innovative triple mixed norm regularizer with strong convexity is utilized to facilitate optimization an iterative solution is derived using the language of duality leading to a low regret bound of o t where t is the number of online iterations this paper details a proof of the algorithm s convergence and describes extensive visual data classification experiments that demonstrate the effectiveness and robustness of this new approach

faster independent component analysis by preconditioning with hessian approximations independent component analysis ica is a technique for unsupervised exploration of multichannel data that is widely used in observational sciences in its classic form ica relies on modeling the data as linear mixtures of nongaussian independent sources the maximization of the corresponding likelihood is a challenging problem if it has to be completed quickly and accurately on large sets of real data this problem has been addressed by resorting to quasi newton methods which rely on sparse approximations of the hessian of the log likelihood however those approximations are not accurate when the ica model does not hold exactly as is often the case for real datasets we propose a new algorithm dubbed picard which makes use of sparse approximate hessians only as a preconditioner to the l bfgs algorithm refining the hessian approximation from a memory of the past iterates extensive numerical comparisons to several algorithms of the same class demonstrate the superior performance of the proposed technique especially on real data

a geometric approach to shape from defocus we introduce a novel approach to shape from defocus i e the problem of inferring the three dimensional 3d geometry of a scene from a collection of defocused images typically in shape from defocus the task of extracting geometry also requires deblurring the given images a common approach to bypass this task relies on approximating the scene locally by a plane parallel to the image the so called equifocal assumption we show that this approximation is indeed not necessary as one can estimate 3d geometry while avoiding deblurring without strong assumptions on the scene solving the problem of shape from defocus requires modeling how light interacts with the optics before reaching the imaging surface this interaction is described by the so called point spread function psf when the form of the psf is known we propose an optimal method to infer 3d geometry from defocused images that involves computing orthogonal operators which are regularized via functional singular value decomposition when the form of the psf is unknown we propose a simple and efficient method that first learns a set of projection operators from blurred images and then uses these operators to estimate the 3d geometry of the scene from novel blurred images our experiments on both real and synthetic images show that the performance of the algorithm is relatively insensitive to the form of the psf our general approach is to minimize the euclidean norm of the difference between the estimated images and the observed images the method is geometric in that we reduce the minimization to performing projections onto linear subspaces by using inner product structures on both infinite and finite dimensional hilbert spaces both proposed algorithms involve only simple matrix vector multiplications which can be implemented in real time

multi image deblurring using complementary sets of fluttering patterns we present a novel coded exposure video technique for multi image motion deblurring the key idea of this paper is to capture video frames with a set of complementary fluttering patterns which enables us to preserve all spectrum bands of a latent image and recover a sharp latent image to achieve this we introduce an algorithm for generating a complementary set of binary sequences based on the modern communication theory and implement the coded exposure video system with an off the shelf machine vision camera to demonstrate the effectiveness of our method we provide in depth analyses of the theoretical bounds and the spectral gains of our method and other state of the art computational imaging approaches we further show deblurring results on various challenging examples with quantitative and qualitative comparisons to other computational image capturing methods used for image deblurring and show how our method can be applied for protecting privacy in videos

re weighted discriminatively embedded k means for multi view clustering recent years more and more multi view data are widely used in many real world applications this kind of data such as image data is high dimensional and obtained from different feature extractors which represents distinct perspectives of the data how to cluster such data efficiently is a challenge in this paper we propose a novel multi view clustering framework called re weighted discriminatively embedded k means for this task the proposed method is a multi view least absolute residual model which induces robustness to efficiently mitigates the influence of outliers and realizes dimension reduction during multi view clustering specifically the proposed model is an unsupervised optimization scheme which utilizes iterative re weighted least squares to solve least absolute residual and adaptively controls the distribution of multiple weights in a re weighted manner only based on its own low dimensional subspaces and a common clustering indicator matrix furthermore theoretical analysis including optimality and convergence analysis and the optimization algorithm are also presented compared with several state of the art multi view clustering methods the proposed method substantially improves the accuracy of the clustering results on widely used benchmark data sets which demonstrates the superiority of the proposed work

a polynomial characterization of hypergraphs using the ihara zeta function the aim of this paper is to seek a compact characterization of irregular unweighted hypergraphs for the purposes of clustering to this end we develop a polynomial characterization for hypergraphs based on the ihara zeta function we investigate the flexibility of the polynomial coefficients for learning relational structures with different relational orders furthermore we develop an efficient method for computing the coefficient set our representation for hypergraphs takes into account not only the vertex connections but also the hyperedge cardinalities and thus can distinguish different relational orders which is prone to ambiguity if the hypergraph laplacian is used in our experimental evaluation we demonstrate the effectiveness of the proposed characterization for clustering irregular unweighted hypergraphs and its advantages over the spectral characterization of the hypergraph laplacian

learning descriptive visual representation for image classification and annotation abstract this paper presents a novel semantic regularized matrix factorization method for learning descriptive visual bag of words bow representation although very influential in image classification the traditional visual bow representation has one distinct drawback that is for efficiency purposes this visual representation is often generated by directly clustering the low level visual feature vectors extracted from local keypoints or regions without considering the high level semantics of images in other words it still suffers from the semantic gap and may lead to significant performance degradation in more challenging tasks e g image classification over social collections with large intra class variations to learn descriptive visual bow representation for such image classification task we develop a semantic regularized matrix factorization method by adding laplacian regularization defined with the tags easy to access of social images into matrix factorization moreover given that image annotation only provides the tags of training images in advance while the tags of all social images are available we can readily apply the proposed method to image annotation by first running a round of image annotation to predict the tags maybe incorrect of test images and thus obtaining the tags of all images experimental results show the promising performance of the proposed method

geometric invariant features in the radon transform domain for near duplicate image detection radon transform has been widely used in content based image representation due to its excellent geometric properties in this paper we propose a family of geometric invariant features based on radon transform for near duplicate image detection according to the theoretical analysis between geometric operations translation scaling and rotation and radon transform we present a geometric invariant feature model based on the feature model we developed four kinds of geometric invariant features in addition a uniform sampling technique is introduced to combine different features the comprehensive performance of the combined feature is better than that of a single one extensive experiments show that the proposed features are robust not only to rotation and scaling but also to other operations such as compression noise contamination blurring illumination modification cropping etc and achieve strong competitive performance compared with the state of the art image features