pairwise rotation invariant co occurrence local binary pattern designing effective features is a fundamental problem in computer vision however it is usually difficult to achieve a great tradeoff between discriminative power and robustness previous works shown that spatial co occurrence can boost the discriminative power of features however the current existing co occurrence features are taking few considerations to the robustness and hence suffering from sensitivity to geometric and photometric variations in this work we study the transform invariance ti of co occurrence features concretely we formally introduce a pairwise transform invariance pti principle and then propose a novel pairwise rotation invariant co occurrence local binary pattern pricolbp feature and further extend it to incorporate multi scale multi orientation and multi channel information different from other lbp variants pricolbp can not only capture the spatial context co occurrence information effectively but also possess rotation invariance we evaluate pricolbp comprehensively on nine benchmark data sets from five different perspectives e g encoding strategy rotation invariance the number of templates speed and discriminative power compared to other lbp variants furthermore we apply pricolbp to six different but related applications texture material flower leaf food and scene classification and demonstrate that pricolbp is efficient effective and of a well balanced tradeoff between the discriminative power and robustness

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

visual word spatial arrangement for image retrieval and classification we present word spatial arrangement wsa an approach to represent the spatial arrangement of visual words under the bag of visual words model it lies in a simple idea which encodes the relative position of visual words by splitting the image space into quadrants using each detected point as origin wsa generates compact feature vectors and is flexible for being used for image retrieval and classification for working with hard or soft assignment requiring no pre post processing for spatial verification experiments in the retrieval scenario show the superiority of wsa in relation to spatial pyramids experiments in the classification scenario show a reasonable compromise between those methods with spatial pyramids generating larger feature vectors while wsa provides adequate performance with much more compact features as wsa encodes only the spatial information of visual words and not their frequency of occurrence the results indicate the importance of such information for visual categorization highlightsspatial arrangement of visual words wsa for image retrieval and classification wsa generates vectors more compact than the traditional spatial pooling methods wsa outperforms spatial pyramids in the retrieval scenario wsa presents adequate performance in the classification scenario

linear distance coding for image classification the feature coding pooling framework is shown to perform well in image classification tasks because it can generate discriminative and robust image representations the unavoidable information loss incurred by feature quantization in the coding process and the undesired dependence of pooling on the image spatial layout however may severely limit the classification in this paper we propose a linear distance coding ldc method to capture the discriminative information lost in traditional coding methods while simultaneously alleviating the dependence of pooling on the image spatial layout the core of the ldc lies in transforming local features of an image into more discriminative distance vectors where the robust image to class distance is employed these distance vectors are further encoded into sparse codes to capture the salient features of the image the ldc is theoretically and experimentally shown to be complementary to the traditional coding methods and thus their combination can achieve higher classification accuracy we demonstrate the effectiveness of ldc on six data sets two of each of three types specific object scene and general object i e flower 102 and pfid 61 scene 15 and indoor 67 caltech 101 and caltech 256 the results show that our method generally outperforms the traditional coding methods and achieves or is comparable to the state of the art performance on these data sets

texture information in run length matrices we use a multilevel dominant eigenvector estimation algorithm to develop a new run length texture feature extraction algorithm that preserves much of the texture information in run length matrices and significantly improves image classification accuracy over traditional run length techniques the advantage of this approach is demonstrated experimentally by the classification of two texture data sets comparisons with other methods demonstrate that the run length matrices contain great discriminatory information and that a good method of extracting such information is of paramount importance to successful classification

texture classification using non euclidean minkowski dilation abstract this study presents a new method to extract meaningful descriptors of gray scale texture images using minkowski morphological dilation based on the l p metric the proposed approach is motivated by the success previously achieved by bouligand minkowski fractal descriptors on texture classification in essence such descriptors are directly derived from the morphological dilation of a three dimensional representation of the gray level pixels using the classical euclidean metric in this way we generalize the dilation for different values of p in the l p metric euclidean is a particular case when p 2 and obtain the descriptors from the cumulated distribution of the distance transform computed over the texture image the proposed method is compared to other state of the art approaches such as local binary patterns and textons for example in the classification of two benchmark data sets uiuc and outex the proposed descriptors outperformed all the other approaches in terms of rate of images correctly classified the interesting results suggest the potential of these descriptors in this type of task with a wide range of possible applications to real world problems

keypoint selection for efficient bag of words feature generation and effective image classification one of the most popular image representations for image classification is based on the bag of words bow features however the number of keypoints that need to be detected from images to generate the bow features is usually very large which causes two problems first the computational cost during the vector quantization step is high second some of the detected keypoints are not helpful for recognition to resolve these limitations we introduce a framework called iterative keypoint selection iks with which to select representative keypoints for accelerating the computational time to generate the bow features leading to more discriminative feature representation each iteration in iks is comprised of two steps in the first step some representative keypoint s are identified from each image then the keypoints are filtered out if the distances between them and the identified representative keypoint s are less than a pre defined distance the iteration process continues until no unrepresentative keypoints can be found two specific approaches are proposed to perform the first step of iks iks1 focuses on randomly selecting one representative keypoint and iks2 is based on a clustering algorithm in which the representative keypoints are the closest points to their cluster centers experiments carried out based on the caltech 101 caltech 256 and pascal 2007 datasets demonstrate that performing keypoint selection using iks1 and iks2 to generate both the bow and spatial based bow features allows the support vector machine svm classifier to provide better classification accuracy than with the baseline features without keypoint selection however it is found that the computational cost of iks1 is larger than the baseline methods on the other hand iks2 is able to not only efficiently generate the bow and spatial based features that reduce the computational time for vector quantization over these datasets but also provides better classification results than iks1 over the pascal 2007 and caltech 256 datasets

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

rotation invariant local frequency descriptors for texture classification this paper presents a novel rotation invariant method for texture classification based on local frequency components the local frequency components are computed by applying 1 d fourier transform on a neighboring function defined on a circle of radius r at each pixel we observed that the low frequency components are the major constituents of the circular functions and can effectively represent textures three sets of features are extracted from the low frequency components two based on the phase and one based on the magnitude the proposed features are invariant to rotation and linear changes of illumination moreover by using low frequency components the proposed features are very robust to noise while the proposed method uses a relatively small number of features it outperforms state of the art methods in three well known datasets brodatz outex and curet in addition the proposed method is very robust to noise and can remarkably improve the classification accuracy especially in the presence of high levels of noise

ergodicity of finite energy diffusions recently the existence of a class of diffusion processes with highly singular drift coefficients has been established under the assumption of finite energy the drift singularities of these diffusions greatly complicate their ergodicity properties indeed they can render the diffusion nonergodic in this paper a method is given for estimating the relaxation time of a finite energy diffusion when it is ergodic these results are applied to show that the set of spin2 diffusions of stochastic mechanics is uniformly ergodic 2

modelling the collapse transition in branched polymers abstract we consider a number of lattice animal models for the collapse transition exhibited by branched polymers in dilute solution in all models the collapse is driven by either some kind of near neighbour fugacity or cycle fugacity we describe rigorous upper and lower bounds on the temperature dependence of the limiting free energy and compare these bounds with numerical estimates derived using new exact enumeration data finally we comment on the difficulties associated with the interpretation of results for the specific heat

hypotheses for image features icons and textons we review ideas about the relationship between qualitative description of local image structure and quantitative description based on responses to a family of linear filters we propose a sequence of three linking hypotheses the first the feature hypothesis is that qualitative descriptions arise from a category system on filter response space the second the icon hypothesis is that the partitioning into categories of filter response space is determined by a system of iconic images one associated with each point of the space the third the texton hypothesis is that the correct images to play the role of icons are those that are the most likely explanations of a vector of filter responses we present results in support of these three hypotheses including new results on 2 d 1st order structure

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

rotation invariant texture classification using lbp variance lbpv with global matching local or global rotation invariant feature extraction has been widely used in texture classification local invariant features e g local binary pattern lbp have the drawback of losing global spatial information while global features preserve little local texture information this paper proposes an alternative hybrid scheme globally rotation invariant matching with locally variant lbp texture features using lbp distribution we first estimate the principal orientations of the texture image and then use them to align lbp histograms the aligned histograms are then in turn used to measure the dissimilarity between images a new texture descriptor lbp variance lbpv is proposed to characterize the local contrast information into the one dimensional lbp histogram lbpv does not need any quantization and it is totally training free to further speed up the proposed matching scheme we propose a method to reduce feature dimensions using distance measurement the experimental results on representative databases show that the proposed lbpv operator and global matching scheme can achieve significant improvement sometimes more than 10 in terms of classification accuracy over traditional locally rotation invariant lbp method

contextual noise reduction for domain adaptive near duplicate retrieval on merchandize images in this paper we have proposed a novel method which utilizes the contextual relationship among visual words for reducing the quantization errors in near duplicate image retrieval ndr instead of following the track of conventional ndr techniques which usually search new solutions by borrowing ideas from the text domain we propose to model the problem back to image domain which results in a more natural way of solution search the idea of the proposed method is to construct a context graph that encapsulates the contextual relationship within an image and treat the graph as a pseudo image so that classical image filters can be adopted to reduce the mis mapped visual words which are contextually inconsistent with others with these contextual noises reduced the method provides purified inputs to the subsequent processes in ndr and improves the overall accuracy more importantly the purification further increases the sparsity of the image feature vectors which thus speeds up the conventional methods by 1662 times and makes ndr practical to online applications on merchandize images where the requirement of response time is critical the way of considering contextual noise reduction in image domain also makes the problem open to all sophisticated filters our study shows the classic anisotropic diffusion filter can be employed to address the cross domain issue resulting in the superiority of the method to conventional ones in both effectiveness and efficiency

a statistical approach to texture classification from single images we investigate texture classification from single images obtained under unknown viewpoint and illumination a statistical approach is developed where textures are modelled by the joint probability distribution of filter responses this distribution is represented by the frequency histogram of filter response cluster centres textons recognition proceeds from single uncalibrated images and the novelty here is that rotationally invariant filters are used and the filter response space is low dimensional classification performance is compared with the filter banks and methods of leung and malik ijcv 2001 schmid cvpr 2001 and cula and dana ijcv 2004 and it is demonstrated that superior performance is achieved here classification results are presented for all 61 materials in the columbia utrecht texture database we also discuss the effects of various parameters on our classification algorithm such as the choice of filter bank and rotational invariance the size of the texton dictionary as well as the number of training images used finally we present a method of reliably measuring relative orientation co occurrence statistics in a rotationally invariant manner and discuss whether incorporating such information can enhance the classifier s performance

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

the impact of large scale computing on lattice statistics the use of computers in theoretical physics has grown dramatically over the years this is as true in lattice statistical mechanics as anywhere this paper is concerned with one such aspect with which the name of c domb has been closely associated the enumeration of embeddings of connected structures in an unlimited crystal lattice an informal account is given of a recent computer project originating in the combinatorial shadow method developed by m f sykes the determination of the numbers and the properties ofcluster embeddings in crystal lattices sykes approach has opened a way to information which was earlier considered to be forever beyond reach the principles are given and the algorithms sketched the detailed fortran programming is not given the methods used have had to be specially developed but some have a wider application for computer algebra when the computational task is massive provided the computer is large enough and fast enough impressive results may be obtained in return for a reasonable effort in practice this implies that the computer may have to be one of the largest and fastest or else that it is dedicated to the task

rotation invariant image and video description with local binary pattern features in this paper we propose a novel approach to compute rotation invariant features from histograms of local noninvariant patterns we apply this approach to both static and dynamic local binary pattern lbp descriptors for static texture description we present lbp histogram fourier lbp hf features and for dynamic texture recognition we present two rotation invariant descriptors computed from the lbps from three orthogonal planes lbp top features in the spatiotemporal domain lbp hf is a novel rotation invariant image descriptor computed from discrete fourier transforms of lbp histograms the approach can be also generalized to embed any uniform features into this framework and combining the supplementary information e g sign and magnitude components of the lbp together can improve the description ability moreover two variants of rotation invariant descriptors are proposed to the lbp top which is an effective descriptor for dynamic texture recognition as shown by its recent success in different application problems but it is not rotation invariant in the experiments it is shown that the lbp hf and its extensions outperform noninvariant and earlier versions of the rotation invariant lbp in the rotation invariant texture classification in experiments on two dynamic texture databases with rotations or view variations the proposed video features can effectively deal with rotation variations of dynamic textures dts they also are robust with respect to changes in viewpoint outperforming recent methods proposed for view invariant recognition of dts

learning rotation invariant local binary descriptor in this paper we propose a rotation invariant local binary descriptor ri lbd learning method for visual recognition compared with hand crafted local binary descriptors such as local binary pattern and its variants which require strong prior knowledge local binary feature learning methods are more efficient and data adaptive unlike existing learning based local binary descriptors such as compact binary face descriptor and simultaneous local binary feature learning and encoding which are susceptible to rotations our ri lbd first categorizes each local patch into a rotational binary pattern rbp and then jointly learns the orientation for each pattern and the projection matrix to obtain ri lbds as all the rotation variants of a patch belong to the same rbp they are rotated into the same orientation and projected into the same binary descriptor then we construct a codebook by a clustering method on the learned binary codes and obtain a histogram feature for each image as the final representation in order to exploit higher order statistical information we extend our ri lbd to the triple rotation invariant co occurrence local binary descriptor trico lbd learning method which learns a triple co occurrence binary code for each local patch extensive experimental results on four different visual recognition tasks including image patch matching texture classification face recognition and scene classification show that our ri lbd and trico lbd outperform most existing local descriptors

generating functions for connected embeddings in a lattice v application to the simple cubic and body centred cubic lattices for pt iv see ibid vol 19 p 2431 1986 the method of partial generating functions is used to derive for the simple cubic lattice the number of connected strong embeddings through 13 sites the number of connected weak embeddings through 14 bonds and three new bond perimeter polynomials d10 d11 d12 for the bond percolation problem for the body centred cubic and simple cubic lattices an expression is derived for the mean number of clusters for the site percolation problem in powers of the probabilities of occupation of a and b sites

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

generating functions for connected embeddings in a lattice i strong embeddings the method of partial generating functions developed to derive high field expansions for the ising model enumerates unrestricted strong embeddings in a lattice the method is modified to enumerate connected embeddings only an explicit general receipt is given for the relation between unrestricted and restricted generating functions for the body centred cubic lattice the number of connected strong embeddings of clusters with up to 13 sites is derived

local binary circumferential and radial derivative pattern for texture classification motivated by gradients polar coordinate representation and its discrete approximation investigating the theoretical scheme of local differential approximation to build feature circumferential and radial derivatives pattern to capture tangential and radial information of derivatives respectively new descriptor is built based on radial plus tangential components and derivative information our approach produces high classification performance on several texture databases building discriminative and robust texture representation to deal with the changes of texture appearance is a fundamental issue in texture classification the local binary pattern lbp and its variants gain a lot of attention during the past decade and achieve great success in texture description however the current existing lbp based features which treat lbp as local differential or orientation gradient operator exploited local orientation pattern or anisotropic structure information separately in this paper we investigate the theoretical scheme of local differential approximation on the polar coordinate system in order to build a new lbp based descriptor which better takes into account both radial plus tangential components and derivative information first we present an operator called circumferential derivative cd based on the tangential information with different order of derivatives then we present an operator called radial derivative rd based on the radial information with different order of derivatives both extract complementary information locally around a central pixel a new descriptor the local binary circumferential and radial derivative pattern crdp is constructed to fuse both local circumferential and radial derivative features based on different orders as well as a global feature based on global difference gd of central pixels intensity extensive experiments on outex curet kth tips and kth tips2 a texture datasets indicate that the proposed crdp descriptor is discriminative and robust the results obtained by the proposed crdp descriptor outperforms more than twenty recent lbp based state of the art methods including the best reported results in the literature for aforementioned texture datasets to the best of our knowledge

recursive nearest agglomeration rena fast clustering for approximation of structured signals in this work we revisit fast dimension reduction approaches as with random projections and random sampling our goal is to summarize the data to decrease computational costs and memory footprint of subsequent analysis such dimension reduction can be very efficient when the signals of interest have a strong structure such as with images we focus on this setting and investigate feature clustering schemes for data reductions that capture this structure an impediment to fast dimension reduction is then that good clustering comes with large algorithmic costs we address it by contributing a linear time agglomerative clustering scheme recursive nearest agglomeration rena unlike existing fast agglomerative schemes it avoids the creation of giant clusters we empirically validate that it approximates the data as well as traditional variance minimizing clustering schemes that have a quadratic complexity in addition we analyze signal approximation with feature clustering and show that it can remove noise improving subsequent analysis steps as a consequence data reduction by clustering features with rena yields very fast and accurate models enabling to process large datasets on budget our theoretical analysis is backed by extensive experiments on publicly available data that illustrate the computation efficiency and the denoising properties of the resulting dimension reduction scheme

perimeter polynomials for bond percolation processes perimeter polynomials are given for the bond percolation problem on the following lattices the triangular up to d9 simple quadratic d13 honeycomb d17 face centred cubic d7 body centred cubic d8 simple cubic d9 and diamond d13 the total number of bond clusters grouped by size is given to two further orders in each case

a completed modeling of local binary pattern operator for texture classification in this correspondence a completed modeling of the local binary pattern lbp operator is proposed and an associated completed lbp clbp scheme is developed for texture classification a local region is represented by its center pixel and a local difference sign magnitude transform ldsmt the center pixels represent the image gray level and they are converted into a binary code namely clbp center clbp c by global thresholding ldsmt decomposes the image local differences into two complementary components the signs and the magnitudes and two operators namely clbp sign clbp s and clbp magnitude clbp m are proposed to code them the traditional lbp is equivalent to the clbp s part of clbp and we show that clbp s preserves more information of the local structure than clbp m which explains why the simple lbp operator can extract the texture features reasonably well by combining clbp s clbp m and clbp c features into joint or hybrid distributions significant improvement can be made for rotation invariant texture classification

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

perceptual uniform descriptor and ranking on manifold for image retrieval abstract incompatibility of image descriptor and ranking has been often neglected in image retrieval in this paper manifold learning and gestalt psychology theory are involved to solve the problem of incompatibility a new holistic descriptor called perceptual uniform descriptor pud based on gestalt psychology is proposed which combines color and gradient direction to imitate human visual uniformity pud features in the same class images distributes on one manifold in most cases as pud improves the visual uniformity of the traditional descriptors thus we use manifold ranking and pud to realize image retrieval experiments were carried out on four benchmark data sets and the proposed method is shown to greatly improve the accuracy of image retrieval our experimental results in ukbench and corel 1k datasets demonstrate that n s score reached 3 58 hsv 3 4 and map at 81 77 odbtc 77 9 respectively by utilizing pud which has only 280 dimensions the results are higher than other holistic image descriptors including local ones as well as state of the arts retrieval methods

image analysis by fractional order orthogonal moments orthogonal moments play an important role in image analysis and other similar applications however existing orthogonal moments are restricted to integer order and little investigation of non integer order orthogonal moments has been conducted to date in this paper a general framework of real order orthogonal moments also known as fractional order orthogonal moments is proposed in this general framework fractional order orthogonal moments can be defined in cartesian and polar coordinate systems shifted legendre polynomials are implemented in this paper to investigate the properties of fractional order orthogonal moments a series of experiments are performed which demonstrate that fractional order orthogonal moments are not only capable of region of interest roi feature extraction but also have potential for image reconstruction and face recognition and have high noise robustness in invariant image recognition

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

local binary features for texture classification local binary patterns lbp have emerged as one of the most prominent and widely studied local texture descriptors truly a large number of lbp variants has been proposed to the point that it can become overwhelming to grasp their respective strengths and weaknesses and there is a need for a comprehensive study regarding the prominent lbp related strategies new types of descriptors based on multistage convolutional networks and deep learning have also emerged in different papers the performance comparison of the proposed methods to earlier approaches is mainly done with some well known texture datasets with differing classifiers and testing protocols and often not using the best sets of parameter values and multiple scales for the comparative methods very important aspects such as computational complexity and effects of poor image quality are often neglected in this paper we provide a systematic review of current lbp variants and propose a taxonomy to more clearly group the prominent alternatives merits and demerits of the various lbp features and their underlying connections are also analyzed we perform a large scale performance evaluation for texture classification empirically assessing forty texture features including thirty two recent most promising lbp variants and eight non lbp descriptors based on deep convolutional networks on thirteen widely used texture datasets the experiments are designed to measure their robustness against different classification challenges including changes in rotation scale illumination viewpoint number of classes different types of image degradation and computational complexity the best overall performance is obtained for the median robust extended local binary pattern mrelbp feature for textures with very large appearance variations fisher vector pooling of deep convolutional neural networks is clearly the best but at the cost of very high computational complexity the sensitivity to image degradations and computational complexity are among the key problems for most of the methods considered highlightsa taxonomy and comprehensive survey of lbp variants characteristics of and connections between lbp variants are provided a comprehensive experimental evaluation of 32 lbp methods comparison of 32 lbp variants with 8 deep convnets features evaluation of robustness to rotation illumination scale and noise changes comparison of computational complexity of forty variants

on convergence properties of the em algorithm for gaussian mixtures we build up the mathematical connection between the expectation maximization em algorithm and gradient based approaches for maximum likelihood learning of finite gaussian mixtures we show that the em step in parameter space is obtained from the gradient via a projection matrix p and we provide an explicit expression for the matrix we then analyze the convergence of em in terms of special properties of p and provide new results analyzing the effect that p has on the likelihood surface based on these mathematical results we present a comparative discussion of the advantages and disadvantages of em and other algorithms for the learning of gaussian mixture models

osri a rotationally invariant binary descriptor binary descriptors are becoming widely used in computer vision field because of their high matching efficiency and low memory requirements since conventional approaches which first compute a floating point descriptor then binarize it are computationally expensive some recent efforts have focused on directly computing binary descriptors from local image patches although these binary descriptors enable a significant speedup in processing time their performances usually drop a lot due to orientation estimation errors and limited description abilities to address these issues we propose a novel binary descriptor based on the ordinal and spatial information of regional invariants osris over a rotation invariant sampling pattern our main contributions are twofold 1 each bit in osri is computed based on difference tests of regional invariants over pairwise sampling regions instead of difference tests of pixel intensities commonly used in existing binary descriptors which can significantly enhance the discriminative ability and 2 rotation and illumination changes are handled well by ordering pixels according to their intensities and gradient orientations meanwhile which is also more reliable than those methods that resort to a reference orientation for rotation invariance besides a statistical analysis of discriminative abilities of different parts in the descriptor is conducted to design a cascade filter which can reject nonmatching descriptors at early stages by comparing just a small portion of the whole descriptor further reducing the matching time extensive experiments on four challenging data sets oxford 53 objects zubud and kentucky show that osri significantly outperforms two state of the art binary descriptors freak and orb the matching performance of osri with only 512 bits is also better than the well known floating point descriptor sift 4k bits and is comparable with the state of the art floating point descriptor mrogh 6k bits while it is two orders of magnitude faster to match than sift and mrogh

ranking consistency for image matching and object retrieval the goal of object retrieval is to rank a set of images by the similarity of their contents to those of a query image however it is difficult to measure image content similarity due to visual changes caused by varying viewpoint and environment in this paper we propose a simple efficient method to more effectively measure content similarity from image measurements our method is based on the ranking information available from existing retrieval systems we observe that images within the set which when used as queries yield similar ranking lists are likely to be relevant to each other and vice versa in our method ranking consistency is used as a verification method to efficiently refine an existing ranking list in much the same fashion that spatial verification is employed the efficiency of our method is achieved by a list wise min hash scheme which allows rapid calculation of an approximate similarity ranking experimental results demonstrate the effectiveness of the proposed framework and its applications highlightswe propose an image matching and retrieval framework based on ranking consistency a list wise min hash method ensures the efficiency of ranking verification our method is flexible and thus has a variety of retrieval related applications

the collapse transition for the lattice trees the authors consider a lattice tree model for the collapse of dilute branched polymers in the good solvent regime in which the collapse is driven by a near neighbour contact fugacity they describe some rigorous results including bounds for the temperature dependence of the reduced limiting free energy and compare these results with numerical estimates derived from exact enumeration data from the specific heat they estimate the collapse temperature tc and the cross over exponent phi 0 in two and three dimensions they find phi 0 0 60 or 0 03 d 2 and phi 0 0 82 or 0 03 d 3 finally they speculate on a possible roughening transition which may occur at a temperature tr tc

derivation of series expansions for a study of percolation processes the derivation of series expansions for a study of percolation processes for both site and bond mixtures is reviewed for the bond problem low density expansions for the mean size of clusters on the simple cubic and body centred cubic lattices are given through p14 high density expansions for the mean number and size of finite clusters and the percolation probability are given for the simple cubic lattice through q41 and for the body centred cubic lattice through q61

texture classification using dominant neighborhood structure this paper proposes a new approach to extract global image features for the purpose of texture classification the proposed texture features are obtained by generating an estimated global map representing the measured intensity similarity between any given image pixel and its surrounding neighbors within a certain window the intensity similarity map is an average representation of the texture image dominant neighborhood similarity the estimated dominant neighborhood similarity is robust to noise and referred to as image dominant neighborhood structure the global rotation invariant features are then extracted from the generated image dominant neighborhood structure features obtained from the local binary patterns lbps are then extracted in order to supply additional local texture features to the generated features from the dominant neighborhood structure both features complement each other the experimental results on representative texture databases show that the proposed method is robust to noise and can achieve significant improvement in terms of the obtained classification accuracy in comparison to the lbp method in addition the method classification accuracy is comparable to the two recent lbp extensions dominant lbp and completed lbp

bi partite percolation and gelation bi partite percolation is discussed in the context of gelation the partial generating function method is used to derive expansions for the mean number of clusters and for the weight average molecular weight in terms of the reactant concentrations a and b renormalisation group and other arguments imply that the critical behaviour in bi partite percolation is determined by the ordinary percolation fixed point at a b pc

on unifying multi view self representations for clustering by tensor multi rank minimization in this paper we address the multi view subspace clustering problem our method utilizes the circulant algebra for tensor which is constructed by stacking the subspace representation matrices of different views and then rotating to capture the low rank tensor subspace so that the refinement of the view specific subspaces can be achieved as well as the high order correlations underlying multi view data can be explored by introducing a recently proposed tensor factorization namely tensor singular value decomposition t svd kilmer et al in siam j matrix anal appl 34 1 148 172 2013 we can impose a new type of low rank tensor constraint on the rotated tensor to ensure the consensus among multiple views different from traditional unfolding based tensor norm this low rank tensor constraint has optimality properties similar to that of matrix rank derived from svd so the complementary information can be explored and propagated among all the views more thoroughly and effectively the established model called t svd based multi view subspace clustering t svd msc falls into the applicable scope of augmented lagrangian method and its minimization problem can be efficiently solved with theoretical convergence guarantee and relatively low computational complexity extensive experimental testing on eight challenging image datasets shows that the proposed method has achieved highly competent objective performance compared to several state of the art multi view clustering methods

interferences in match kernels we consider the design of an image representation that embeds and aggregates a set of local descriptors into a single vector popular representations of this kind include the bag of visual words the fisher vector and the vlad when two such image representations are compared with the dot product the image to image similarity can be interpreted as a match kernel in match kernels one has to deal with interference i e with the fact that even if two descriptors are unrelated their matching score may contribute to the overall similarity we formalise this problem and propose two related solutions both aimed at equalising the individual contributions of the local descriptors in the final representation these methods modify the aggregation stage by including a set of per descriptor weights they differ by the objective function that is optimised to compute those weights the first is a democratisation strategy that aims at equalising the relative importance of each descriptor in the set comparison metric the second one involves equalising the match of a single descriptor to the aggregated vector these concurrent methods give a substantial performance boost over the state of the art in image search with short or mid size vectors as demonstrated by our experiments on standard public image retrieval benchmarks

multiple metric learning based on bar shape descriptor for person re identification abstract the robust structural feature extraction and similarity measure play critical roles in person re identification this paper presents a novel algorithm named multiple metric learning based on bar shape descriptor mmlbd for person re identification specifically we first propose a new multiple bar shape descriptor that can take full account of the spatial correlation between the center points and their adjacent points on different directions it captures further histogram features based on a novel color difference weight factors with an overlapping sliding window which can depict the local variations and consistency in the whole image the similarity and dissimilarity of samples are used to train the weight factor of features and an optimal subspace could be obtained at the same time next we provide an effective multiple metric learning method fusing two channel bar shape structural features via the optimal similarity pairwise measure obtained by a dissimilarity matrix this measure can fully mine the discriminative information and eliminate redundancy in the similar features which make the mmlbd simple and effective finally evaluation experiments on the i lids caviar4reid and ward data sets are carried out which compare the proposed mmlbd with the corresponding methods experimental results demonstrate that the mmlbd is more effective and robust against visual appearance variations

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

hep 2 cell classification using shape index histograms with donut shaped spatial pooling we present a new method for automatic classification of indirect immunoflourescence images of hep 2 cells into different staining pattern classes our method is based on a new texture measure called shape index histograms that captures second order image structure at multiple scales moreover we introduce a spatial decomposition scheme which is radially symmetric and suitable for cell images the spatial decomposition is performed using donut shaped pooling regions of varying sizes when gathering histogram contributions we evaluate our method using both the icip 2013 and the icpr 2012 competition datasets our results show that shape index histograms are superior to other popular texture descriptors for hep 2 cell classification moreover when comparing to other automated systems for hep 2 cell classification we show that shape index histograms are very competitive especially considering the relatively low complexity of the method

identification rate search and memory complexity tradeoff fundamental limits in an information theoretic framework we introduce a two stage decoding scheme capable of achieving identification capacity to address search and memory complexities in large scale identification systems this two stage decoding procedure is accomplished as follows for a given query at the first stage a list of cluster indices is estimated then at the second stage refinement checks are performed for all the members of the clusters to produce a single index the first result this paper presents is the achievable rate quadruple region that specifies necessary conditions that the two stage decoding scheme should satisfy to be able to achieve the identification capacity the rest of this paper is designated to investigate various achievable rate quadruples in which the proposed two stage identification setup can reduce the search complexity with respect to conventional identification setups

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

high order local ternary patterns with locality preserving projection for smoke detection and image classification it is a challenging task to recognize smoke from visual scenes due to large variations in the color texture shapes of smoke to improve detection accuracy we propose a novel feature extraction method by encoding high order directional derivatives at each pixel we first quantize the directional derivatives into ternary values to generate local ternary patterns ltp for the sake of simplification each ltp code is usually decomposed into an upper lbp code and a lower lbp code but this leads to loss of information hence we use joint histograms to preserve the co occurrence of upper and lower lbp codes for each order ltp then we concatenate all joint histograms from different orders to propose high order local ternary patterns hltp to improve computational efficiency we apply locality preserving projection lpp to reduce the dimension of hltp to further improve performance we present a noise resistant mechanism to remove noisy derivatives and then propose hltp based on magnitudes of noise removed derivatives and values of center pixels hltpmc finally the support vector machine svm is used for training and classification experiments on large scale smoke data sets show that our method can achieve detection rates above 94 with false alarm rates below 1 33 experiments on a multi class brodatz texture data set also achieved good performance with low dimensional features so our method has powerful discriminative capabilities and compact feature representation for multi class image classification

diffuse interface models on graphs for classification of high dimensional data this paper is a republication of an mms paper a l bertozzi and a flenner multiscale model simul 10 2012 pp 1090 1118 describing a new class of algorithms for classification of high dimensional data these methods combine ideas from spectral methods on graphs with nonlinear edge region detection methods traditionally used in the pde based imaging community the algorithms use the ginzburg landau functional extended to a graphical framework which has classical pde connections to total variation minimization convex splitting algorithms allow us to quickly find minimizers of the proposed model and take advantage of fast spectral solvers of linear graph theoretic problems we review the diverse computational examples presented in the original paper involving both basic clustering and semisupervised learning for different applications case studies include feature identification in images segmentation in social networks and segmentation of shapes in high dimensional datasets since the pape

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

texture classification using local pattern based on vector quantization local binary pattern lbp is a simple and effective descriptor for texture classification however it has two main disadvantages 1 different structural patterns sometimes have the same binary code and 2 it is sensitive to noise in order to overcome these disadvantages we propose a new local descriptor named local vector quantization pattern lvqp in lvqp different kinds of texture images are chosen to train a local pattern codebook where each different structural pattern is described by a unique codeword index contrarily to the original lbp and its many variants lvqp does not quantize each neighborhood pixel separately to 0 1 but aims at quantizing the whole difference vector between the central pixel and its neighborhood pixels since lvqp deals with the structural pattern as a whole it has a high discriminability and is less sensitive to noise our experimental results achieved by using four representative texture databases of outex uiuc curet and brodatz show that the proposed lvqp method can improve classification accuracy significantly and is more robust to noise

hep 2 staining pattern recognition using stacked fisher network for encoding weber local descriptor abstract this study addresses the recognition problem of the hep 2 cell using indirect immunofluorescent iif image analysis which can indicate the presence of autoimmune diseases by finding antibodies in the patient serum generally the method used for iif analysis remains subjective and depends too heavily on the experience and expertise of the physician this study aims to explore an automatic hep 2 cell recognition system in which how to extract highly discriminate visual features plays a key role in this recognition application in order to realize this purpose our main efforts include 1 a simple but robust local descriptor without any quantization for local patch representation 2 a transformation of the difference values between the surrounding pixels and the center one to the perception degree which is based on the fact that human perception for disguising a pattern depends not only on the absolute intensity of the stimulus but also on the relative variance of the stimulus called as weber local descriptor wld 3 a data driven coding strategy with a parametric probability process and the extraction of not only low but also high order statistics for image representation called as fisher vector 4 the stacking of the fisher network into multi layer framework for more discriminate feature experiments using the open hep 2 cell dataset released in the icip2013 contest validate that the proposed strategy can achieve a much better performance than the state of the art approaches and that the achieved recognition error rate is even very significantly below the observed intra laboratory variability

hep 2 cell image classification with multiple linear descriptors abstract the automatic classification of the hep 2 cell stain patterns from indirect immunofluorescence images has attracted much attention recently as an image classification problem it can be well solved by the state of the art bag of features bof model as long as a suitable local descriptor is known unfortunately for this special task we have very limited knowledge of such a descriptor in this paper we explore the possibility of automatically learning the descriptor from the image data itself specifically we assume that a local patch can be well described by a set of linear projections performed on its pixel values based on this assumption both unsupervised and supervised approaches are explored for learning the projections more importantly we propose a multi projection multi codebook scheme which creates multiple linear projection descriptors and multiple image representation channels with each channel corresponding to one descriptor through our analysis we show that the image representation obtained by combining these different channels can be more discriminative than that obtained from a single projection scheme this analysis is further verified by our experimental study we evaluate the proposed approach by strictly following the protocol suggested by the organizer of the 2012 hep 2 cell classification contest which is hosted to compare the state of the art methods for hep 2 cell classification in this paper our system achieves 66 6 cell level classification accuracy which is just slightly lower than the best performance achieved in the hep 2 cell classification contest this result is impressive and promising considering that we only utilize a single type of feature namely linear projection coefficients of patch pixel values which is learned from the image data

local generating functions for the enumeration of connected embeddings in a lattice a general formalism for the practical application of sykes partial generating function method 1986 is given the formalism is suitable for the study of problems where the generating functions depend on detailed local properties and is constructed in a manner which allows easy programming of the method

a new distance measure for non identical data with application to image classification distance measures are part and parcel of many computer vision algorithms the underlying assumption in all existing distance measures is that feature elements are independent and identically distributed however in real world settings data generally originate from heterogeneous sources even if they do possess a common data generating mechanism since these sources are not identically distributed by necessity the assumption of identical distribution is inappropriate here we use statistical analysis to show that feature elements of local image descriptors are indeed non identically distributed to test the effect of omitting the unified distribution assumption we created a new distance measure called the poisson binomial radius pbr pbr is a bin to bin distance which accounts for the dispersion of bin to bin information pbr s performance was evaluated on twelve benchmark data sets covering six different classification and recognition applications texture material leaf scene ear biometrics and category level image classification results from these experiments demonstrate that pbr outperforms state of the art distance measures for most of the data sets and achieves comparable performance on the rest suggesting that accounting for different distributions in distance measures can improve performance in classification and recognition tasks highlightsempirical evidence is provided that real world data is non identically distributed pbr the first distance measure to account for non identical data is proposed pbr was tested in 6 test applications using 12 benchmark data sets pbr outperforms state of the art measures for most data sets avoiding the identical distribution assumption can improve classification

fusion of color histogram and lbp based features for texture image retrieval and classification abstract the local binary pattern lbp operator and its variants play an important role as the image feature extractor in the textural image retrieval and classification the lbp based operator extracts the textural information of an image by considering the neighboring pixel values a single or join histogram can be derived from the lbp code which can be used as an image feature descriptor in some applications however the lbp based feature is not a good candidate in capturing the color information of an image making it is less suitable for measuring the similarity of color images with rich color information this work overcomes this problem by adding an additional color feature namely color information feature cif along with the lbp based feature in the image retrieval and classification systems the cif and lbp based feature adequately represent the color and texture features as documented in the experimental result the hybrid cif and lbp based feature presents a promising result and outperforms the existing methods over several image databases thus it can be a very competitive candidate in retrieval and classification application

visual query expansion with or without geometry refining local descriptors by feature aggregation this paper proposes a query expansion technique for image search that is faster and more precise than the existing ones an enriched representation of the query is obtained by exploiting the binary representation offered by the hamming embedding image matching approach the initial local descriptors are refined by aggregating those of the database while new descriptors are produced from the images that are deemed relevant the technique has two computational advantages over other query expansion techniques first the size of the enriched representation is comparable to that of the initial query second the technique is effective even without using any geometry in which case searching a database comprising 105k images typically takes 80 ms on a desktop machine overall our technique significantly outperforms the visual query expansion state of the art on popular benchmarks it is also the first query expansion technique shown effective on the ukb benchmark which has few relevant images per query

median robust extended local binary pattern for texture classification local binary patterns lbp are considered among the most computationally efficient high performance texture features however the lbp method is very sensitive to image noise and is unable to capture macrostructure information to best address these disadvantages in this paper we introduce a novel descriptor for texture classification the median robust extended lbp mrelbp different from the traditional lbp and many lbp variants mrelbp compares regional image medians rather than raw image intensities a multiscale lbp type descriptor is computed by efficiently comparing image medians over a novel sampling scheme which can capture both microstructure and macrostructure texture information a comprehensive evaluation on benchmark data sets reveals mrelbp s high performance robust to gray scale variations rotation changes and noise but at a low computational cost mrelbp produces the best classification scores of 99 82 99 38 and 99 77 on three popular outex test suites more importantly mrelbp is shown to be highly robust to image noise including gaussian noise gaussian blur salt and pepper noise and random pixel corruption

compact representation of high dimensional feature vectors for large scale image recognition and retrieval in large scale visual recognition and image retrieval tasks feature vectors such as fisher vector fv or the vector of locally aggregated descriptors vlad have achieved state of the art results however the combination of the large numbers of examples and high dimensional vectors necessitates dimensionality reduction in order to reduce its storage and cpu costs to a reasonable range in spite of the popularity of various feature compression methods this paper shows that the feature dimension selection is a better choice for high dimensional fv vlad than the feature dimension compression methods e g product quantization we show that strong correlation among the feature dimensions in the fv and the vlad may not exist which renders feature selection a natural choice we also show that many dimensions in fv vlad are noise throwing them away using feature selection is better than compressing them and useful dimensions altogether using feature compression methods to choose features we propose an efficient importance sorting algorithm considering both the supervised and unsupervised cases for visual recognition and image retrieval respectively combining with the 1 bit quantization feature selection has achieved both higher accuracy and less computational cost than feature compression methods such as product quantization on the fv and the vlad image representations

lattice animals supplementation of perimeter polynomial data by graph theoretic methods application of graph theoretic methods to new perimeter polynomials for connected clusters on a lattice yields extra data on the total number of clusters and for the coefficients in the series expansion for the mean size of clusters at low densities the lattices studied are the square the square with next nearest neighbors the triangular and the simple cubic

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

contour and texture analysis for image segmentation this paper provides an algorithm for partitioning grayscale images into disjoint regions of coherent brightness and texture natural images contain both textured and untextured regions so the cues of contour and texture differences are exploited simultaneously contours are treated in the intervening contour framework while texture is analyzed using textons each of these cues has a domain of applicability so to facilitate cue combination we introduce a gating operator based on the texturedness of the neighborhood at a pixel having obtained a local measure of how likely two nearby pixels are to belong to the same region we use the spectral graph theoretic framework of normalized cuts to find partitions of the image into regions of coherent texture and brightness experimental results on a wide range of images are shown

the stochastic mechanics of the pauli equation in stochastic mechanics the bopp haag dankel diffusions on r3 x so 3 are used to represent particles with spin bopp and haag showed that in the limit as the particle s moment of inertia i goes to zero the solutions of the bopp haag equations converge to that of the regular pauli equation nelson has conjectured that in the same limit the projections of the bopp haag dankel 3 diffusions onto r converge to a markovian limit process in this paper we prove this conjecture for spin 2 and regular potentials and identify the limit process as the diffusion naturally associated with the solution to the regular pauli equation

image search with selective match kernels aggregation across single and multiple images this paper considers a family of metrics to compare images based on their local descriptors it encompasses the vector or locally aggregated descriptors descriptor and matching techniques such as hamming embedding making the bridge between these approaches leads us to propose a match kernel that takes the best of existing techniques by combining an aggregation procedure with a selective match kernel the representation underpinning this kernel is approximated providing a large scale image search both precise and scalable as shown by our experiments on several benchmarks we show that the same aggregation procedure originally applied per image can effectively operate on groups of similar features found across multiple images this method implicitly performs feature set augmentation while enjoying savings in memory requirements at the same time finally the proposed method is shown effective for place recognition outperforming state of the art methods on a large scale landmark recognition benchmark

generating functions for connected embeddings in a lattice iii bond percolation for pt ii see ibid vol 19 p 1027 32 1986 the method of partial generating functions is developed to obtain bond perimeter polynomials for percolation processes four new polynomials d9 d12 are given for the body centered cubic lattice

hep 2 cell classification using rotation invariant co occurrence among local binary patterns abstract this paper proposes a novel method for classifying six categories of patterns of fluorescence staining of a hep 2 cell the proposed method is constructed as a combination of the powerful rotation invariant co occurrence among adjacent local binary pattern ric lbp image feature and a linear support vector machine svm ric lbp provides high descriptive ability and robustness against local rotations of an input cell image to further deal with global rotation we synthesize many training images by rotating the original training images and constructing the svm using both the original and synthesized images the proposed method has the following advantages 1 robustness against uniform changes in intensity of an input cell image 2 invariance under local and global rotation of the image 3 low computational cost and 4 easy implementation the proposed method was demonstrated to be effective through evaluation experiments using the mivia hep 2 images dataset and comparison with typical state of the art methods

exploring illumination robust descriptors for human epithelial type 2 cell classification strong illumination variation is a key challenge in the human epithelial type 2 hep 2 cell classification task aiming to improve the robustness of the hep 2 classification system to the illumination variation this paper deeply explores discriminative and illumination robust descriptors specifically we propose a novel spatial shape index descriptor ssid to capture spatial layout information of the second order structures and utilize a local orientation adaptive descriptor load which was originally designed for texture classification to the hep 2 cell classification task both ssid and load show strong discrimination and great complementarity to each other four different sets of experiments were carried out to evaluate ssid load and their combination our two submissions achieved superior performance on the new executable thematic of pattern recognition techniques for indirect immunofluorescence images analysis compared to the rank 1st method in the icpr 2014 hep 2 cell classification contest both of our submissions achieved a better performance when only using the provided training data our approaches also demonstrated superior performance on a newly compiled large scale hep 2 data set with 63 445 cell images highlightstwo introduced features achieve superior performance on the hep 2 cell classification task we propose a novel spatial shape index descriptor ssid to capture spatial layout of the second order texture structures we utilize a multi scale local orientation adaptive descriptor load to the hep 2 cell classification task we introduce a new large scale hep 2 data set that contains 63 445 cell images from the i3a task 2 data set

label propagation based supervised locality projection analysis for plant leaf classification the label propagation has the benefits of nearly linear running time and easy implementation in this paper we make use of the label propagation to propose a new weight measure and present a supervised locality projection analysis slpa method for plant leaf classification firstly we apply warshall algorithm to label propagation and get the label matrix then incorporate it into the weight which has a clear physical meaning secondly multi class data points in high dimensional space are to be pulled or pushed by discriminant neighbors to form an optimum projecting to low dimensionality finally the experimental results on two plant leaf databases show that the proposed method is quite effective and feasible

hough pyramid matching speeded up geometry re ranking for large scale image retrieval exploiting local feature shape has made geometry indexing possible but at a high cost of index space while a sequential spatial verification and re ranking stage is still indispensable for large scale image retrieval in this work we investigate an accelerated approach for the latter problem we develop a simple spatial matching model inspired by hough voting in the transformation space where votes arise from single feature correspondences using a histogram pyramid we effectively compute pair wise affinities of correspondences without ever enumerating all pairs our hough pyramid matching algorithm is linear in the number of correspondences and allows for multiple matching surfaces or non rigid objects under one to one mapping we achieve re ranking one order of magnitude more images at the same query time with superior performance compared to state of the art methods while requiring the same index space we show that soft assignment is compatible with this matching scheme preserving one to one mapping and further increasing performance

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

mixture of subspaces image representation and compact coding for large scale image retrieval there are two major approaches to content based image retrieval using local image descriptors one is descriptor by descriptor matching and the other is based on comparison of global image representation that describes the set of local descriptors of each image in large scale problems the latter is preferred due to its smaller memory requirements however it tends to be inferior to the former in terms of retrieval accuracy to achieve both low memory cost and high accuracy we investigate an asymmetric approach in which the probability distribution of local descriptors is modeled for each individual database image while the local descriptors of a query are used as is we adopt a mixture model of probabilistic principal component analysis the model parameters constitute a global image representation to be stored in database then the likelihood function is employed to compute a matching score between each database image and a query we also propose an algorithm to encode our image representation into more compact codes experimental results demonstrate that our method can represent each database image in less than several hundred bytes achieving higher retrieval accuracy than the state of the art method using fisher vectors

diffusion processes with singular drift fields a class of stochastic differential equations with highly singular drift fields is considered using a purely probabilistic approach we can show the unattainability of the nodal set moreover a global existence and uniqueness theorem for diffusion processes with singular drift fields is established the finite action condition of carlen and zheng can be modified we relate our results to the diffusions which describe the time evolution of quantum systems in stochastic mechanics