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

shrinkage priors for bayesian prediction we investigate shrinkage priors for constructing bayesian predictive distributions it is shown that there exist shrinkage predictive distributions asymptotically dominating bayesian predictive distributions based on the jeffreys prior or other vague priors if the model manifold satisfies some differential geometric conditions kullback leibler divergence from the true distribution to a predictive distribution is adopted as a loss function conformal transformations of model manifolds corresponding to vague priors are introduced we show several examples where shrinkage predictive distributions dominate bayesian predictive distributions based on vague priors

rotation and gray scale transform invariant texture identification using wavelet decomposition and hidden markov model in this correspondence we have presented a rotation and gray scale transform invariant texture recognition scheme using the combination of quadrature mirror filter qmf bank and hidden markov model hmm in the first stage the qmf bank is used as the wavelet transform to decompose the texture image into subbands the gray scale transform invariant features derived from the statistics based on first order distribution of gray levels are then extracted from each subband image in the second stage the sequence of subbands is modeled as a hidden markov model hmm and one hmm is designed for each class of textures the hmm is used to exploit the dependence among these subbands and is able to capture the trend of changes caused by rotation during recognition the unknown texture is matched against all the models the best matched model identifies the texture class up to 93 33 classification accuracy is reported

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

handbook of statistics 16 order statistics theory and methods 2000 handbook of statistics 16 order statistics theory and methods technometrics vol 42 no 4 pp 445 445

brint binary rotation invariant and noise tolerant texture classification in this paper we propose a simple efficient yet robust multiresolution approach to texture classification binary rotation invariant and noise tolerant brint the proposed approach is very fast to build very compact while remaining robust to illumination variations rotation changes and noise we develop a novel and simple strategy to compute a local binary descriptor based on the conventional local binary pattern lbp approach preserving the advantageous characteristics of uniform lbp points are sampled in a circular neighborhood but keeping the number of bins in a single scale lbp histogram constant and small such that arbitrarily large circular neighborhoods can be sampled and compactly encoded over a number of scales there is no necessity to learn a texton dictionary as in methods based on clustering and no tuning of parameters is required to deal with different data sets extensive experimental results on representative texture databases show that the proposed brint not only demonstrates superior performance to a number of recent state of the art lbp variants under normal conditions but also performs significantly and consistently better in presence of noise due to its high distinctiveness and robustness this noise robustness characteristic of the proposed brint is evaluated quantitatively with different artificially generated types and levels of noise including gaussian salt and pepper and speckle noise in natural texture images

geometry of deformed exponential families invariant dually flat and conformal geometries an information geometrical foundation is established for the deformed exponential families of probability distributions two different types of geometrical structures an invariant geometry and a flat geometry are given to a manifold of a deformed exponential family the two different geometries provide respective quantities such as deformed free energies entropies and divergences the class belonging to both the invariant and flat geometries at the same time consists of exponential and mixture families theq families are characterized from the viewpoint of the invariant and flat geometries the q exponential family is a unique class that has the invariant and flat geometries in the extended class of positive measures furthermore it is the only class of which the riemannian metric is conformally connected with the invariant fisher metric

estimation of the inverse weibull parameters under adaptive type ii progressive hybrid censoring scheme this paper describes the frequentist and bayesian estimation for the scale parameter and shape parameter of the inverse weibull iw distribution based on adaptive type ii progressive hybrid censoring scheme at ii phcs we discuss the maximum likelihood estimators mles and the approximate mles where the mles cannot be obtained in closed forms the bayes estimates for the iw parameters are obtained based on squared error se loss function by using the approximation form of lindley 1980 the optimal censoring scheme has been suggested using two different optimality criteria a real life data set is used for illustration purpose finally the different proposed estimators have been compared through an extensive simulation studies

robust rotation invariant texture classification using a model based approach in this paper a model based texture classification procedure is presented the texture is modeled as the output of a linear system driven by a binary image this latter retains the morphological characteristics of the texture and it is specified by its spatial autocorrelation function acf we show that features extracted from the acf of the binary excitation suffice to represent the texture for classification purposes specifically we employ a moment invariants based technique to classify the acf the resulting proposed classification procedure is thus inherently rotation invariant moreover it is robust with respect to additive noise experimental results show that this approach allows obtaining high correct rotation invariant classification rates while containing the size of the feature space

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

an mgf based unified framework to determine the joint statistics of partial sums of ordered random variables order statistics find applications in various areas of communications and signal processing in this paper we introduce an unified analytical framework to determine the joint statistics of partial sums of ordered random variables rvs with the proposed approach we can systematically derive the joint statistics of any partial sums of ordered statistics in terms of the moment generating function mgf and the probability density function pdf our mgf based approach applies not only when all the k ordered rvs are involved but also when only the ks ks k best rvs are considered in addition we present the closed form expressions for the exponential rv special case these results apply to the performance analysis of various wireless communication systems over fading channels

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

enhanced local texture feature sets for face recognition under difficult lighting conditions making recognition more reliable under uncontrolled lighting conditions is one of the most important challenges for practical face recognition systems we tackle this by combining the strengths of robust illumination normalization local texture based face representations distance transform based matching kernel based feature extraction and multiple feature fusion specifically we make three main contributions 1 we present a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition 2 we introduce local ternary patterns ltp a generalization of the local binary pattern lbp local texture descriptor that is more discriminant and less sensitive to noise in uniform regions and we show that replacing comparisons based on local spatial histograms with a distance transform based similarity metric further improves the performance of lbp ltp based face recognition and 3 we further improve robustness by adding kernel principal component analysis pca feature extraction and incorporating rich local appearance cues from two complementary sources gabor wavelets and lbp showing that the combination is considerably more accurate than either feature set alone the resulting method provides state of the art performance on three data sets that are widely used for testing recognition under difficult illumination conditions extended yale b cas peal r1 and face recognition grand challenge version 2 experiment 4 frgc 204 for example on the challenging frgc 204 data set it halves the error rate relative to previously published methods achieving a face verification rate of 88 1 at 0 1 false accept rate further experiments show that our preprocessing method outperforms several existing preprocessors for a range of feature sets data sets and lighting conditions

rotation invariant texture classification using a complete space frequency model a method of rotation invariant texture classification based on a complete space frequency model is introduced a polar analytic form of a two dimensional 2 d gabor wavelet is developed and a multiresolution family of these wavelets is used to compute information conserving microfeatures from these microfeatures a micromodel which characterizes spatially localized amplitude frequency and directional behavior of the texture is formed the essential characteristics of a texture sample its macrofeatures are derived from the estimated selected parameters of the micromodel classification of texture samples is based on the macromodel derived from a rotation invariant subset of macrofeatures in experiments comparatively high correct classification rates were obtained using large sample sets

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

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

geometry of an accelerated model with censored data from the geometrical point of view a statistical model can be considered as a manifold with parameter plays the role of a coordinate system in this paper the geometrical quantities of a statistical model equipped with k step stress accelerated life test and progressively type ii censoring are obtained where censoring is allowed not only at each stress change time but also at each failure time as an application of these quantities the asymptotic expansions of the bayesian prediction are investigated finally some computation and simulation results are presented to illustrate our main results

gaussian mrf rotation invariant features for image classification features based on markov random field mrf models are sensitive to texture rotation this paper develops an anisotropic circular gaussian mrf acgmrf model for retrieving rotation invariant texture features to overcome the singularity problem of the least squares estimate method an approximate least squares estimate method is designed and implemented rotation invariant features are obtained from the acgmrf model parameters using the discrete fourier transform the acgmrf model is demonstrated to be a statistical improvement over three published methods the three methods include a laplacian pyramid an isotropic circular gmrf icgmrf and gray level cooccurrence probability features

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

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

the rotation number for almost periodic potentials we define and analyze the rotation number for the almost periodic schrodinger operatorl d2 dx2 q x we use the rotation number to discuss i the spectrum ofl ii its relation to the korteweg de vries equation

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

geometry of exponential family with competing risks and censored data abstract employing the differential geometrical methods in statistics suggested by amari 1985 and amari et al 1987 considering the exponential family with censored data and competing risks as a manifold of a statistical model the geometry of the manifold is investigated based on two information sources as an application of the geometry the asymptotic expansions of the bootstrap prediction bayesian prediction and their risk evaluations are investigated the results show that these expansions are related to the coefficients of connections and metric tensors and the predictive density function is the estimative density function in the asymptotic sense finally taking rayleigh distribution and prostatic cancer data as examples some computation and simulation results are presented to illustrate our main results

discriminative learning of local image descriptors in this paper we explore methods for learning local image descriptors from training data we describe a set of building blocks for constructing descriptors which can be combined together and jointly optimized so as to minimize the error of a nearest neighbor classifier we consider both linear and nonlinear transforms with dimensionality reduction and make use of discriminant learning techniques such as linear discriminant analysis lda and powell minimization to solve for the parameters using these techniques we obtain descriptors that exceed state of the art performance with low dimensionality in addition to new experiments and recommendations for descriptor learning we are also making available a new and realistic ground truth data set based on multiview stereo data

a fast learning algorithm for deep belief nets we show how to use complementary priors to eliminate the explaining away effects that make inference difficult in densely connected belief nets that have many hidden layers using complementary priors we derive a fast greedy algorithm that can learn deep directed belief networks one layer at a time provided the top two layers form an undirected associative memory the fast greedy algorithm is used to initialize a slower learning procedure that fine tunes the weights using a contrastive version of the wake sleep algorithm after fine tuning a network with three hidden layers forms a very good generative model of the joint distribution of handwritten digit images and their labels this generative model gives better digit classification than the best discriminative learning algorithms the low dimensional manifolds on which the digits lie are modeled by long ravines in the free energy landscape of the top level associative memory and it is easy to explore these ravines by using the directed connections to display what the associative memory has in mind

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

geometry on the statistical manifold induced by the degradation model with soft failure data abstract information geometry discusses the properties of a statistical manifold and is useful for different fields involving neural networks signal processing machine learning optimization and statistics the methods of information geometry are also employed to discuss the geometry on a statistical manifold induced by the reliability model and lifetime testing where the time to failure data are available for analyzing for highly reliable products however it is difficult to obtain failure data in a reasonable period of time for some products there is a gradual loss of performance then it is possible to derive degradation measurements over times this paper investigates the geometry on a statistical manifold induced by the degradation model with few or no failure data the statistical manifold is constructed based on the degradation model the fisher information metric amari chentsov structure affine connection and connection on the manifold are discussed taking the linear model as an example the main results are illustrated where we find that the geometry quantities are closely related to the degradation threshold value parameters of the model and the euler s constant the parameters are estimated by using the multi step estimation method and numerical results are reported

illumination insensitive image representation via synergistic weighted center surround receptive field model and weber law a novel illumination insensitive texture feature descriptor weber synergistic center surround pattern is presented we model differential synergistic excitation pattern synergistic straight orientation pattern and synergistic diagonal orientation pattern we discuss different scale and direction weighted coefficients in detail we mine higher order spatial information of weighted wscp image representation under complex illumination condition is a great challenge in computer vision field motivated by weber law and synergistic center surround receipt field model we propose an illumination insensitive feature descriptor named as weber synergistic center surround pattern wscp including three components differential synergistic excitation pattern dsep synergistic straight orientation pattern ssop and synergistic diagonal orientation pattern sdop to further enhance the discriminative power of wscp we present a scale and orientation weighted wscp wwscp which fully considers the inner and outer layer pixels excitation distributions as well as their orientation information importance to acquire more discriminative and rich features we utilize the spatiograms to describe patterns instead of the conventional histograms which can obtain higher order spatial information in the final classification stage we present a novel distance measurement model called weighted similarity measurement model wsmm to improve classification accuracy by sufficiently utilizing the information contents and orientation distributions of each pattern extensive experimental comparisons between our methods and other state of the art methods are conducted on cmupie yale b yale b ext feret photex alot and rawfoot databases and performance results have verified the effectiveness and efficiency of our proposed methods on the robustness to illumination variations

bayesian duality and risk analysis on the statistical manifold of exponential family with censored data abstract information geometry has been attracted wide attentions in the past few decades this paper focuses on the bayesian duality on a statistical manifold derived from the exponential family with data from life tests based on life testing data the statistical manifold is constructed with a new cumulant generating function the bregman divergence between two parameter points is studied the dual coordinate system and dual function are obtained then the dualistic structure on the manifold is discussed the results show that the maximum likelihood estimate can be obtained by minimizing the bregman divergence induced from the dual function the bayesian analysis and prediction are investigated based on informative and non informative priors consider the gamma distribution as an example the closed form representations of the dual coordinate system and dual function are obtained a real data set is employed to illustrate the methodologies and experimental designs developed in this paper

locally rotation contrast and scale invariant descriptors for texture analysis textures within real images vary in brightness contrast scale and skew as imaging conditions change to enable recognition of textures in real images it is necessary to employ a similarity measure that is invariant to these properties furthermore since textures often appear on undulating surfaces such invariances must necessarily be local rather than global despite these requirements it is only relatively recently that texture recognition algorithms with local scale and affine invariance properties have begun to be reported typically they comprise detecting feature points followed by geometric normalization prior to description we describe a method based on invariant combinations of linear filters unlike previous methods we introduce a novel family of filters which provides scale invariance resulting in a texture description invariant to local changes in orientation contrast and scale and robust to local skew significantly the family of filters enables local scale invariants to be defined without using a scale selection principle or a large number of filters a texture discrimination method based on the chi2 similarity measure applied to histograms derived from our filter responses outperforms existing methods for retrieval and classification results for both the brodatz textures and the university of illinois urbana champaign uiuc database which has been designed to require local invariance

texture classification by wavelet packet signatures this correspondence introduces a new approach to characterize textures at multiple scales the performance of wavelet packet spaces are measured in terms of sensitivity and selectivity for the classification of twenty five natural textures both energy and entropy metrics were computed for each wavelet packet and incorporated into distinct scale space representations where each wavelet packet channel reflected a specific scale and orientation sensitivity wavelet packet representations for twenty five natural textures were classified without error by a simple two layer network classifier an analyzing function of large regularity d sub 20 was shown to be slightly more efficient in representation and discrimination than a similar function with fewer vanishing moments d sub 6 in addition energy representations computed from the standard wavelet decomposition alone 17 features provided classification without error for the twenty five textures included in our study the reliability exhibited by texture signatures based on wavelet packets analysis suggest that the multiresolution properties of such transforms are beneficial for accomplishing segmentation classification and subtle discrimination of texture

descriptor learning via supervised manifold regularization for multioutput regression multioutput regression has recently shown great ability to solve challenging problems in both computer vision and medical image analysis however due to the huge image variability and ambiguity it is fundamentally challenging to handle the highly complex input target relationship of multioutput regression especially with indiscriminate high dimensional representations in this paper we propose a novel supervised descriptor learning sdl algorithm for multioutput regression which can establish discriminative and compact feature representations to improve the multivariate estimation performance the sdl is formulated as generalized low rank approximations of matrices with a supervised manifold regularization the sdl is able to simultaneously extract discriminative features closely related to multivariate targets and remove irrelevant and redundant information by transforming raw features into a new low dimensional space aligned to targets the achieved discriminative while compact descriptor largely reduces the variability and ambiguity for multioutput regression which enables more accurate and efficient multivariate estimation we conduct extensive evaluation of the proposed sdl on both synthetic data and real world multioutput regression tasks for both computer vision and medical image analysis experimental results have shown that the proposed sdl can achieve high multivariate estimation accuracy on all tasks and largely outperforms the algorithms in the state of the arts our method establishes a novel sdl framework for multioutput regression which can be widely used to boost the performance in different applications

radon transform orientation estimation for rotation invariant texture analysis this paper presents a new approach to rotation invariant texture classification the proposed approach benefits from the fact that most of the texture patterns either have directionality anisotropic textures or are not with a specific direction isotropic textures the wavelet energy features of the directional textures change significantly when the image is rotated however for the isotropic images the wavelet features are not sensitive to rotation therefore for the directional textures it is essential to calculate the wavelet features along a specific direction in the proposed approach the radon transform is first employed to detect the principal direction of the texture then the texture is rotated to place its principal direction at 0 degrees a wavelet transform is applied to the rotated image to extract texture features this approach provides a features space with small intraclass variability and therefore good separation between different classes the performance of the method is evaluated using three texture sets experimental results show the superiority of the proposed approach compared with some existing methods

dominant local binary patterns for texture classification this paper proposes a novel approach to extract image features for texture classification the proposed features are robust to image rotation less sensitive to histogram equalization and noise it comprises of two sets of features dominant local binary patterns dlbp in a texture image and the supplementary features extracted by using the circularly symmetric gabor filter responses the dominant local binary pattern method makes use of the most frequently occurred patterns to capture descriptive textural information while the gabor based features aim at supplying additional global textural information to the dlbp features through experiments the proposed approach has been intensively evaluated by applying a large number of classification tests to histogram equalized randomly rotated and noise corrupted images in outex brodatz meastex and curet texture image databases our method has also been compared with six published texture features in the experiments it is experimentally demonstrated that the proposed method achieves the highest classification accuracy in various texture databases and image conditions

multi dimensional morse index theorems and a symplectic view of elliptic boundary value problems morse index theorems for elliptic boundary value problems in multi dimensions are proved under various boundary conditions the theorems work for star shaped domains and are based on a new idea of measuring the oscillation of the trace of the set of solutions on a shrinking boundary the oscillation is measured by formulating a maslov index in an appropriate sobolev space of functions on this boundary a fundamental difference between the cases of dirichlet and neumann boundary conditions is exposed through a monotonicity that holds only in the former case

enhanced patterns of oriented edge magnitudes for face recognition and image matching a good feature descriptor is desired to be discriminative robust and computationally inexpensive in both terms of time and storage requirement in the domain of face recognition these properties allow the system to quickly deliver high recognition results to the end user motivated by the recent feature descriptor called patterns of oriented edge magnitudes poem which balances the three concerns this paper aims at enhancing its performance with respect to all these criteria to this end we first optimize the parameters of poem and then apply the whitened principal component analysis dimensionality reduction technique to get a more compact robust and discriminative descriptor for face recognition the efficiency of our algorithm is proved by strong results obtained on both constrained face recognition technology feret and unconstrained labeled faces in the wild lfw data sets in addition with the low complexity impressively our algorithm is about 30 times faster than those based on gabor filters furthermore by proposing an additional technique that makes our descriptor robust to rotation we validate its efficiency for the task of image matching

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

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

the formal definition of reference priors reference analysis produces objective bayesian inference in the sense that inferential statements depend only on the assumed model and the available data and the prior distribution used to make an inference is least informative in a certain information theoretic sense reference priors have been rigorously defined in specific contexts and heuristically defined in general but a rigorous general definition has been lacking we produce a rigorous general definition here and then show how an explicit expression for the reference prior can be obtained under very weak regularity conditions the explicit expression can be used to derive new reference priors both analytically and numerically

illumination invariant single face image recognition under heterogeneous lighting condition illumination problem is still a bottleneck of robust face recognition system which demands extracting illumination invariant features in this field existing works only consider the variations caused by lighting direction or magnitude denoted as homogeneous lighting but the effect of spectral wavelength is always ignored and thus existing illumination invariant descriptors have its limitation on processing face images under different spectral wavelengths denoted as heterogeneous lighting we propose a novel gradient based descriptor namely logarithm gradient histogram lgh which takes the illumination direction magnitude and the spectral wavelength together into consideration so that it can handle both homogeneous and heterogeneous lightings our proposal contributes in three folds 1 we incorporate lmsn log filter to eliminate the lighting effect of each image and extract two illumination invariant components namely logarithm gradient orientation lgo and logarithm gradient magnitude lgm 2 we propose an effective post processing strategy to make our model tolerant to noise and generate a histogram representation to integrate both lgo and lgm 3 we present solid theoretical analysis on the illumination invariant properties of our proposed descriptors extensive experimental results on cmu pie extended yaleb frgc and hfb databases are reported to verify the effectiveness of our proposed model highlightstwo illumination invariant components namely logarithm gradient orientation lgo and logarithm gradient magnitude lgm are extracted an effective post processing strategy is proposed to integrate both lgo and lgm generating the logarithm gradient histogram lgh solid theoretical analysis on the illumination invariant properties of the proposed descriptors is presented competitive results are reported both in homogeneous and heterogeneous lighting conditions

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

discriminative deep metric learning for face and kinship verification this paper presents a new discriminative deep metric learning ddml method for face and kinship verification in wild conditions while metric learning has achieved reasonably good performance in face and kinship verification most existing metric learning methods aim to learn a single mahalanobis distance metric to maximize the inter class variations and minimize the intra class variations which cannot capture the nonlinear manifold where face images usually lie on to address this we propose a ddml method to train a deep neural network to learn a set of hierarchical nonlinear transformations to project face pairs into the same latent feature space under which the distance of each positive pair is reduced and that of each negative pair is enlarged to better use the commonality of multiple feature descriptors to make all the features more robust for face and kinship verification we develop a discriminative deep multi metric learning method to jointly learn multiple neural networks under which the correlation of different features of each sample is maximized and the distance of each positive pair is reduced and that of each negative pair is enlarged extensive experimental results show that our proposed methods achieve the acceptable results in both face and kinship verification

learning discriminant face descriptor local feature descriptor is an important module for face recognition and those like gabor and local binary patterns lbp have proven effective face descriptors traditionally the form of such local descriptors is predefined in a handcrafted way in this paper we propose a method to learn a discriminant face descriptor dfd in a data driven way the idea is to learn the most discriminant local features that minimize the difference of the features between images of the same person and maximize that between images from different people in particular we propose to enhance the discriminative ability of face representation in three aspects first the discriminant image filters are learned second the optimal neighborhood sampling strategy is soft determined third the dominant patterns are statistically constructed discriminative learning is incorporated to extract effective and robust features we further apply the proposed method to the heterogeneous cross modality face recognition problem and learn dfd in a coupled way coupled dfd or c dfd to reduce the gap between features of heterogeneous face images to improve the performance of this challenging problem extensive experiments on feret cas peal r1 lfw and hfb face databases validate the effectiveness of the proposed dfd learning on both homogeneous and heterogeneous face recognition problems the dfd improves poem and lqp by about 4 5 percent on lfw database and the c dfd enhances the heterogeneous face recognition performance of lbp by over 25 percent

face recognition using laplacianfaces we propose an appearance based face recognition method called the laplacianface approach by using locality preserving projections lpp the face images are mapped into a face subspace for analysis different from principal component analysis pca and linear discriminant analysis lda which effectively see only the euclidean structure of face space lpp finds an embedding that preserves local information and obtains a face subspace that best detects the essential face manifold structure the laplacianfaces are the optimal linear approximations to the eigenfunctions of the laplace beltrami operator on the face manifold in this way the unwanted variations resulting from changes in lighting facial expression and pose may be eliminated or reduced theoretical analysis shows that pca lda and lpp can be obtained from different graph models we compare the proposed laplacianface approach with eigenface and fisherface methods on three different face data sets experimental results suggest that the proposed laplacianface approach provides a better representation and achieves lower error rates in face recognition

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

extended local binary patterns for face recognition a set of six lbp like features derived from local intensities and differences a labeled dominant pattern scheme is proposed to learn salient information utilizing whitened pca to produce more compact robust and discriminative features fused wpca features improves the accuracy and robustness of face recognition proposed face recognition system is highly robust to illumination variations this paper presents a simple and novel yet highly effective approach for robust face recognition using lbp like descriptors based on local accumulated pixel differences angular differences and radial differences the local differences were decomposed into complementary components of signs and magnitudes based on these descriptors we developed labeled dominant patterns where the most frequently occurring patterns and their labels were learned to capture discriminative textural information six histogram features were obtained from each given face image by concatenating spatial histograms extracted from non overlapping subregions a whitened pca technique was used for dimensionality reduction to produce more compact robust and discriminative features which were then fused using the nearest neighbor classifier with euclidean distance as the similarity measure we evaluated the effectiveness of the proposed method on the extended yale b the large scale feret and cas peal r1 databases and found that that the proposed method impressively outperforms other well known systems with a recognition rate of 74 6 on the cas peal r1 lighting probe set

face recognition by exploring information jointly in space scale and orientation information jointly contained in image space scale and orientation domains can provide rich important clues not seen in either individual of these domains the position spatial frequency and orientation selectivity properties are believed to have an important role in visual perception this paper proposes a novel face representation and recognition approach by exploring information jointly in image space scale and orientation domains specifically the face image is first decomposed into different scale and orientation responses by convolving multiscale and multi orientation gabor filters second local binary pattern analysis is used to describe the neighboring relationship not only in image space but also in different scale and orientation responses this way information from different domains is explored to give a good face representation for recognition discriminant classification is then performed based upon weighted histogram intersection or conditional mutual information with linear discriminant analysis techniques extensive experimental results on feret ar and frgc ver 2 0 databases show the significant advantages of the proposed method over the existing ones

on asymptotic properties of predictive distributions summary second order asymptotic theory of predictive distributions is investigated it is shown that estimative distributions with asymptotically efficient estimators can be improved to predictive distributions that do not belong to the model the model is assumed to be a multidimensional curved exponential family predictive distributions can be constructed by shifting estimative distributions in a direction orthogonal to the model the average kullback leibler divergence from the true distribution to a predictive distribution is represented as the sum of two components one of them depends on the choice of the estimative distribution and the other depends on the shift orthogonal to the model the optimal shift orthogonal to the model is obtained a quantity that can be regarded as the mixture mean curvature of the model in the space of all probability distributions is introduced the difference in average kullback leibler divergence between an estimative distribution and the optimal predictive distribution obtained by shifting it can be regarded as the mixture mean curvature of the model an asymptotic expression for bayesian predictive distributions is also obtained

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

completed local binary count for rotation invariant texture classification in this brief a novel local descriptor named local binary count lbc is proposed for rotation invariant texture classification the proposed lbc can extract the local binary grayscale difference information and totally abandon the local binary structural information although the lbc codes do not represent visual microstructure the statistics of lbc features can represent the local texture effectively in addition a completed lbc clbc is also proposed to enhance the performance of texture classification experimental results obtained from three databases demonstrate that the proposed clbc can achieve comparable accurate classification rates with completed local binary pattern

discriminant waveletfaces and nearest feature classifiers for face recognition feature extraction discriminant analysis and classification rules are three crucial issues for face recognition we present hybrid approaches to handle three issues together for feature extraction we apply the multiresolution wavelet transform to extract the waveletface we also perform the linear discriminant analysis on waveletfaces to reinforce discriminant power during classification the nearest feature plane nfp and nearest feature space nfs classifiers are explored for robust decisions in presence of wide facial variations their relationships to conventional nearest neighbor and nearest feature line classifiers are demonstrated in the experiments the discriminant waveletface incorporated with the nfs classifier achieves the best face recognition performance

filtering for texture classification a comparative study in this paper we review most major filtering approaches to texture feature extraction and perform a comparative study filtering approaches included are laws masks 1980 ring wedge filters dyadic gabor filter banks wavelet transforms wavelet packets and wavelet frames quadrature mirror filters discrete cosine transform eigenfilters optimized gabor filters linear predictors and optimized finite impulse response filters the features are computed as the local energy of the filter responses the effect of the filtering is highlighted keeping the local energy function and the classification algorithm identical for most approaches for reference comparisons with two classical nonfiltering approaches co occurrence statistical and autoregressive model based features are given we present a ranking of the tested approaches based on extensive experiments

geometry of the q exponential distribution with dependent competing risks and accelerated life testing abstract in the information geometry suggested by amari 1985 and amari et al 1987 a parametric statistical model can be regarded as a differentiable manifold with the parameter space as a coordinate system note that the q exponential distribution plays an important role in tsallis statistics see tsallis 2009 this paper investigates the geometry of the q exponential distribution with dependent competing risks and accelerated life testing alt a copula function based on the q exponential function which can be considered as the generalized gumbel copula is discussed to illustrate the structure of the dependent random variable employing two iterative algorithms simulation results are given to compare the performance of estimations and levels of association under different hybrid progressively censoring schemes hpcss

rotation numbers boundary forces and gap labelling we review the johnson moser rotation number and the k0 theoretical gap labelling of bellissard for one dimensional schr odinger operators we compare them with two further gap labels one being related to the motion of dirichlet eigenvalues the other being a k1 theoretical gap label we argue that the latter provides a natural generalization of the johnson moser rotation number to higher dimensions

ldahash improved matching with smaller descriptors sift like local feature descriptors are ubiquitously employed in computer vision applications such as content based retrieval video analysis copy detection object recognition photo tourism and 3d reconstruction feature descriptors can be designed to be invariant to certain classes of photometric and geometric transformations in particular affine and intensity scale transformations however real transformations that an image can undergo can only be approximately modeled in this way and thus most descriptors are only approximately invariant in practice second descriptors are usually high dimensional e g sift is represented as a 128 dimensional vector in large scale retrieval and matching problems this can pose challenges in storing and retrieving descriptor data we map the descriptor vectors into the hamming space in which the hamming metric is used to compare the resulting representations this way we reduce the size of the descriptors by representing them as short binary strings and learn descriptor invariance from examples we show extensive experimental validation demonstrating the advantage of the proposed approach

histogram of gabor phase patterns hgpp a novel object representation approach for face recognition a novel object descriptor histogram of gabor phase pattern hgpp is proposed for robust face recognition in hgpp the quadrant bit codes are first extracted from faces based on the gabor transformation global gabor phase pattern ggpp and local gabor phase pattern lgpp are then proposed to encode the phase variations ggpp captures the variations derived from the orientation changing of gabor wavelet at a given scale frequency while lgpp encodes the local neighborhood variations by using a novel local xor pattern lxp operator they are both divided into the nonoverlapping rectangular regions from which spatial histograms are extracted and concatenated into an extended histogram feature to represent the original image finally the recognition is performed by using the nearest neighbor classifier with histogram intersection as the similarity measurement the features of hgpp lie in two aspects 1 hgpp can describe the general face images robustly without the training procedure 2 hgpp encodes the gabor phase information while most previous face recognition methods exploit the gabor magnitude information in addition fisher separation criterion is further used to improve the performance of hgpp by weighing the subregions of the image according to their discriminative powers the proposed methods are successfully applied to face recognition and the experiment results on the large scale feret and cas peal databases show that the proposed algorithms significantly outperform other well known systems in terms of recognition rate

estimating a product of means bayesian analysis with reference priors abstract suppose that we observe x n 1 and independently y n 1 and are concerned with inference mainly estimation and confidence statements about the product of means this problem arises most obviously in situations of determining area based on measurements of length and width it also arises in other practical contexts however for instance in gypsy moth studies the hatching rate of larvae per unit area can be estimated as the product of the mean of egg masses per unit area times the mean number of larvae hatching per egg mass approximately independent samples can be obtained for each mean see southwood 1978 noninformative prior bayesian approaches to the problem are considered in particular the reference prior approach of bernardo 1979 an appropriate reference prior for the problem is developed and relatively easily implementable formulas for posterior moments e g the posterior mean and variance and credible sets are derived comparisons with alternative noninf

the morse and maslov indices for multidimensional schrodinger operators with matrix valued potentials we study the schrodinger operator l v on a star shaped domain in rd with lipschitz boundary the operator is equipped with quite general dirichletor robin type boundary conditions induced by operators between h1 2 and h 1 2 and the potential takes values in the set of symmetric n n matrices by shrinking the domain and rescaling the operator we obtain a path in the fredholm lagrangian grassmannian of the subspace of h1 2 h 1 2 corresponding to the given boundary condition the path is formed by computing the dirichlet and neumann traces of weak solutions to the rescaled eigenvalue equation we prove a formula relating the number of negative eigenvalues of l the morse index the signed crossings of the path the maslov index the number of negative eigenvalues of the potential matrix evaluated at the center of the domain and the number of negative eigenvalues of a bilinear form related to the boundary operator

correction to rotation and gray scale transform invariant texture classification using spiral resampling subband decomposition and hidden markov model this paper proposes a new texture classification algorithm that is invariant to rotation and gray scale transformation first we convert two dimensional 2 d texture images to one dimensional 1 d signals by spiral resampling then we use a quadrature mirror filter qmf bank to decompose sampled signals into subbands in each band we take high order autocorrelation functions as features features in different bands which form a vector sequence are then modeled as a hidden markov model bmm during classification the unknown texture is matched against all the models and the best match is taken as the classification result simulations showed that the highest correct classification rate for 16 kinds of texture was 95 14

learning kernel extended dictionary for face recognition a sparse representation classifier src and a kernel discriminant analysis kda are two successful methods for face recognition an src is good at dealing with occlusion while a kda does well in suppressing intraclass variations in this paper we propose kernel extended dictionary ked for face recognition which provides an efficient way for combining kda and src we first learn several kernel principal components of occlusion variations as an occlusion model which can represent the possible occlusion variations efficiently then the occlusion model is projected by kda to get the ked which can be computed via the same kernel trick as new testing samples finally we use structured src for classification which is fast as only a small number of atoms are appended to the basic dictionary and the feature dimension is low we also extend ked to multikernel space to fuse different types of features at kernel level experiments are done on several large scale data sets demonstrating that not only does ked get impressive results for nonoccluded samples but it also handles the occlusion well without overfitting even with a single gallery sample per subject

gabor feature based classification using the enhanced fisher linear discriminant model for face recognition this paper introduces a novel gabor fisher 1936 classifier gfc for face recognition the gfc method which is robust to changes in illumination and facial expression applies the enhanced fisher linear discriminant model efm to an augmented gabor feature vector derived from the gabor wavelet representation of face images the novelty of this paper comes from 1 the derivation of an augmented gabor feature vector whose dimensionality is further reduced using the efm by considering both data compression and recognition generalization performance 2 the development of a gabor fisher classifier for multi class problems and 3 extensive performance evaluation studies in particular we performed comparative studies of different similarity measures applied to various classifiers we also performed comparative experimental studies of various face recognition schemes including our novel gfc method the gabor wavelet method the eigenfaces method the fisherfaces method the efm method the combination of gabor and the eigenfaces method and the combination of gabor and the fisherfaces method the feasibility of the new gfc method has been successfully tested on face recognition using 600 feret frontal face images corresponding to 200 subjects which were acquired under variable illumination and facial expressions the novel gfc method achieves 100 accuracy on face recognition using only 62 features

rotation invariant textural feature extraction for image retrieval using eigen value analysis of intensity gradients and multi resolution analysis in this paper a novel rotation invariant multi resolution based texture retrieval technique is proposed the rotation invariance is achieved by aligning the direction of maximum variation of intensity gradient defined as principal texture direction along the reference axis the principal direction is determined using eigen value analysis of gradient image wavelet transform based techniques are applied on the rotated image the independent representation of textural energies along various directions enhances the retrieval performance over the existing rotation invariant wavelet based techniques which achieve rotation invariance by averaging the direction sensitive components extensive experiments on brodatz database support this postulate