rotation invariant texture retrieval considering the scale dependence of gabor wavelet obtaining robust and efficient rotation invariant texture features in content based image retrieval field is a challenging work we propose three efficient rotation invariant methods for texture image retrieval using copula model based in the domains of gabor wavelet gw and circularly symmetric gw csgw the proposed copula models use copula function to capture the scale dependence of gw csgw for improving the retrieval performance it is well known that the kullback leibler distance kld is the commonly used similarity measurement between probability models however it is difficult to deduce the closed form of kld between two copula models due to the complexity of the copula model we also put forward a kind of retrieval scheme using the klds of marginal distributions and the kld of copula function to calculate the kld of copula model the proposed texture retrieval method has low computational complexity and high retrieval precision the experimental results on vistex and brodatz data sets show that the proposed retrieval method is more effective compared with the state of the art methods

a minimax inequality and its applications to ordinary differential equations the aim of this paper is to investigate the minimax inequality which plays a fundamental role in the critical points theorem of b ricceri below equivalent formulations are shown and characterization is proved in particular for a special class of functionals as an application a multiplicity result for an ordinary dirichlet problem is emphasized

lightweight probabilistic texture retrieval this paper contemplates the framework of probabilistic image retrieval in the wavelet domain from a computational point of view we not only focus on achieving high retrieval rates but also discuss possible performance bottlenecks which might prevent practical application we propose a novel retrieval approach which is motivated by previous research work on modeling the marginal distributions of wavelet transform coefficients the building blocks of our work are the dual tree complex wavelet transform and a number of statistical models for the coefficient magnitudes image similarity measurement is accomplished by using closed form solutions for the kullback leibler divergences between the statistical models we provide an in depth computational analysis regarding the number of arithmetic operations required for similarity measurement and model parameter estimation the experimental retrieval results on a widely used texture image database show that we achieve competitive retrieval results at low computational cost

log polar wavelet energy signatures for rotation and scale invariant texture classification classification of texture images is important in image analysis and classification this paper proposes an effective scheme for rotation and scale invariant texture classification using log polar wavelet signatures the rotation and scale invariant feature extraction for a given image involves applying a log polar transform to eliminate the rotation and scale effects but at same time produce a row shifted log polar image which is then passed to an adaptive row shift invariant wavelet packet transform to eliminate the row shift effects so the output wavelet coefficients are rotation and scale invariant the adaptive row shift invariant wavelet packet transform is quite efficient with only o n spl middot log n complexity a feature vector of the most dominant log polar wavelet energy signatures extracted from each subband of wavelet coefficients is constructed for rotation and scale invariant texture classification in the experiments we employed a mahalanobis classifier to classify a set of 25 distinct natural textures selected from the brodatz album the experimental results based on different testing data sets for images with different orientations and scales show that the proposed classification scheme using log polar wavelet signatures outperforms two other texture classification methods its overall accuracy rate for joint rotation and scale invariance being 90 8 percent demonstrating that the extracted energy signatures are effective rotation and scale invariant features concerning its robustness to noise the classification scheme also performs better than the other methods

chi plots for assessing dependence summary this paper presents a graphical method to be used in conjunction with a scatterplot to investigate possible association of two variates as manifested in a sample of bivariate measurements the method is designed so that the plot is approximately horizontal under independence and under various forms of association it produces corresponding characteristic patterns examples given include application to study of regression residuals dependence of two spatial point processes serial association of time series and comparison of two time series

gaussian copula multivariate modeling for texture image retrieval using wavelet transforms in the framework of texture image retrieval a new family of stochastic multivariate modeling is proposed based on gaussian copula and wavelet decompositions we take advantage of the copula paradigm which makes it possible to separate dependence structure from marginal behavior we introduce two new multivariate models using respectively generalized gaussian and weibull densities these models capture both the subband marginal distributions and the correlation between wavelet coefficients we derive as a similarity measure a closed form expression of the jeffrey divergence between gaussian copula based multivariate models experimental results on well known databases show significant improvements in retrieval rates using the proposed method compared with the best known state of the art approaches

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

wavelet based texture retrieval using generalized gaussian density and kullback leibler distance we present a statistical view of the texture retrieval problem by combining the two related tasks namely feature extraction fe and similarity measurement sm into a joint modeling and classification scheme we show that using a consistent estimator of texture model parameters for the fe step followed by computing the kullback leibler distance kld between estimated models for the sm step is asymptotically optimal in term of retrieval error probability the statistical scheme leads to a new wavelet based texture retrieval method that is based on the accurate modeling of the marginal distribution of wavelet coefficients using generalized gaussian density ggd and on the existence a closed form for the kld between ggds the proposed method provides greater accuracy and flexibility in capturing texture information while its simplified form has a close resemblance with the existing methods which uses energy distribution in the frequency domain to identify textures experimental results on a database of 640 texture images indicate that the new method significantly improves retrieval rates e g from 65 to 77 compared with traditional approaches while it retains comparable levels of computational complexity

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

tangent projection equations and general variational inequalities in this paper we establish the equivalence between the general variational inequalities and tangent projection equations this equivalence is used to discuss the local convergence analysis of a wide class of iterative methods for solving the general variational inequalities we show that some existing methods can identify the optimal face after finitely many iterations under the degenerate assumption

rotation invariant multiresolution texture analysis using radon and wavelet transforms a new rotation invariant texture analysis technique using radon and wavelet transforms is proposed this technique utilizes the radon transform to convert the rotation to translation and then applies a translation invariant wavelet transform to the result to extract texture features a k nearest neighbors classifier is employed to classify texture patterns a method to find the optimal number of projections for the radon transform is proposed it is shown that the extracted features generate an efficient orthogonal feature space it is also shown that the proposed features extract both of the local and directional information of the texture patterns the proposed method is robust to additive white noise as a result of summing pixel values to generate projections in the radon transform step to test and evaluate the method we employed several sets of textures along with different wavelet bases experimental results show the superiority of the proposed method and its robustness to additive white noise in comparison with some recent texture analysis methods

self adaptive projection algorithms for general variational inequalities in this paper we consider and analyze a new class of self adaptive projection algorithms for solving general variational inequalities by using the technique of updating the solution we prove that the convergence of these new methods only requires the pseudomonotonicity which is a weaker condition than monotonicity these new methods differ from the previously known splitting methods for solving variational inequalities and related complementarity problems proof of convergence is very simple as special cases we can obtain a number of four step forward backward splitting methods of noor for solving variational inequalities

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

wavelet modeling using finite mixtures of generalized gaussian distributions application to texture discrimination and retrieval this paper addresses statistical based texture modeling using wavelets we propose a new approach to represent the marginal distribution of the wavelet coefficients using finite mixtures of generalized gaussian mogg distributions the mogg captures a wide range of histogram shapes which provides better description and discrimination of texture than using single probability density functions pdf s as proposed by recent state of the art approaches moreover we propose a model similarity measure based on kullback leibler divergence kld approximation using monte carlo sampling methods through experiments on two popular texture data sets we show that our approach yields significant performance improvements for texture discrimination and retrieval as compared with recent methods of statistical based wavelet modeling

color texture image retrieval based on gaussian copula models of gabor wavelets color texture retrieval is a hot research area in image analysis in this paper we propose an efficient color texture retrieval method by using copula model based on gabor wavelets when gabor wavelets are used to decompose color image three types of dependence exist in the decomposed subbands of gabor wavelets color dependence scale dependence and direction dependence we analyze these dependencies and then capture them by using gaussian copula function four copula schemes are developed and accordingly four klds kullback leibler distances of the copula schemes are introduced for color texture image retrieval the evaluations of the proposed method are performed on several color texture databases including two large texture databases alot and stex experimental results demonstrate the proposed method has better performance than the state of the art retrieval methods highlightswe propose a color texture retrieval method by using gabor wavelet and copula model we use copula model to capture the dependencies of gabor wavelet and color channels four copula models are implemented based on gabor wavelet in color space our method shows better performance on large databases than other popular methods

modified resolvent splitting algorithms for general mixed variational inequalities in this paper we suggest and analyze a number of four step resolvent splitting algorithms for solving general mixed variational inequalities by using the updating technique of the solution the convergence of these new methods requires either monotonicity or pseudomonotonicity of the operator proof of convergence is very simple our new methods differ from the existing splitting methods for solving variational inequalities and complementarity problems the new results are versatile and are easy to implement

some developments in general variational inequalities general variational inequalities provide us with a unified natural novel and simple framework to study a wide class of equilibrium problems arising in pure and applied sciences in this paper we present a number of new and known numerical techniques for solving general variational inequalities using various techniques including projection wiener hopf equations updating the solution auxiliary principle inertial proximal penalty function dynamical system and well posedness we also consider the local and global uniqueness of the solution and sensitivity analysis of the general variational inequalities as well as the finite convergence of the projection type algorithms our proofs of convergence are very simple as compared with other methods our results present a significant improvement of previously known methods for solving variational inequalities and related optimization problems since the general variational inequalities include quasi variational inequalities and quasi implicit complementarity problems as special cases results presented here continue to hold for these problems several open problems have been suggested for further research in these areas

some aspects of variational inequalities abstract in this paper we provide an account of some of the fundamental aspects of variational inequalities with major emphasis on the theory of existence uniqueness computational properties various generalizations sensitivity analysis and their applications we also propose some open problems with sufficient information and references so that someone may attempt solution s in his her area of special interest we also include some new results which we have recently obtained

new extragradient type methods for general variational inequalities in this paper we consider and analyze a new class of extragradient type methods for solving general variational inequalities the modified methods converge for pseudomonotone operators which is weaker condition than monotonicity our proof of convergence is very simple as compared with other methods the proposed methods include several new and known methods as special cases our results present a significant improvement of previously known methods for solving variational inequalities and related optimization problems

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