image annotation via graph learning image annotation has been an active research topic in recent years due to its potential impact on both image understanding and web image search in this paper we propose a graph learning framework for image annotation first the image based graph learning is performed to obtain the candidate annotations for each image in order to capture the complex distribution of image data we propose a nearest spanning chain nsc method to construct the image based graph whose edge weights are derived from the chain wise statistical information instead of the traditional pairwise similarities second the word based graph learning is developed to refine the relationships between images and words to get final annotations for each image to enrich the representation of the word based graph we design two types of word correlations based on web search results besides the word co occurrence in the training set the effectiveness of the proposed solution is demonstrated from the experiments on the corel dataset and a web image dataset

periodicity directionality and randomness wold features for image modeling and retrieval one of the fundamental challenges in pattern recognition is choosing a set of features appropriate to a class of problems in applications such as database retrieval it is important that image features used in pattern comparison provide good measures of image perceptual similarities we present an image model with a new set of features that address the challenge of perceptual similarity the model is based on the 2d wold decomposition of homogeneous random fields the three resulting mutually orthogonal subfields have perceptual properties which can be described as periodicity directionality and randomness approximating what are indicated to be the three most important dimensions of human texture perception the method presented improves upon earlier wold based models in its tolerance to a variety of local inhomogeneities which arise in natural textures and its invariance under image transformation such as rotation an image retrieval algorithm based on the new texture model is presented different types of image features are aggregated for similarity comparison by using a bayesian probabilistic approach the effectiveness of the wold model at retrieving perceptually similar natural textures is demonstrated in comparison to that of two other well known pattern recognition methods the wold model appears to offer a perceptually more satisfying measure of pattern similarity while exceeding the performance of these other methods by traditional pattern recognition criteria examples of natural scene wold texture modeling are also presented

a novel multiset integrated canonical correlation analysis framework and its application in feature fusion multiset canonical correlation analysis mcca is difficult to effectively express the integrated correlation among multiple feature vectors in feature fusion thus this paper firstly presents a novel multiset integrated canonical correlation analysis micca framework the micca establishes a discriminant correlation criterion function of multi group variables based on generalized correlation coefficient the criterion function can clearly depict the integrated correlation among multiple feature vectors then the paper presents a multiple feature fusion theory and algorithm using the micca method the detailed process of the algorithm is as follows firstly extract multiple feature vectors from the same patterns by using different feature extraction methods then extract multiset integrated canonical correlation features using micca finally form effective discriminant feature vectors through two given feature fusion strategies for pattern classification the multi group feature fusion method based on micca not only achieves the aim of feature fusion but also removes the redundancy between features the experiment results on cenparmi handwritten arabic numerals and uci multiple features database show that the micca method has better recognition rates and robustness than the fusion methods based on canonical correlation analysis cca and mcca

kolmogorov s test for super brownian motion we prove a kolmogorov test for super brownian motion started at the dirac mass at the origin more precisely we determine the functions g such that for all t small enough the support of the process at time t will be contained in the ball of radius g t centered at 0 as a consequence we get a necessary and sufficient condition for the existence in certain space time domains of a solution of the associated semilinear partial differential equation that blows up at the origin

a class of path valued markov processes and its applications to superprocesses let s be a continuous markov process satisfying certain regularity assumptions we introduce a path valued strong markov process associated with s which is closely related to the so called superprocess with spatial motion s in particular a subseth of the state space of s intersects the range of the superprocess if and only if the set of paths that hith is not polar for the path valued process the latter property can be investigated using the tools of the potential theory of symmetric markov processes a set is not polar if and only if it supports a measure of finite energy the same approach can be applied to study sets that are polar for the graph of the superprocess in the special case when s is a diffusion process we recover certain results recently obtained by dynkin

reduced complexity modeling and reproduction of colored textures an unsupervised color texture synthesis by analysis method is described the texture is reproduced to appear perceptually similar to a given prototype by copying its statistical properties up to the second order the synthesized texture is obtained at the output of a single input three output nonlinear system driven by a realization of a white gaussian random field significant complexity reduction is gained by exploiting the rank deficiency of the cross power spectral density matrix of the color texture samples

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

a no reference texture regularity metric based on visual saliency this paper presents a no reference perceptual metric that quantifies the degree of perceived regularity in textures the metric is based on the similarity of visual attention va of the textural primitives and the periodic spatial distribution of foveated fixation regions throughout the image a ground truth eye tracking database for textures is also generated as part of this paper and is used to evaluate the performance of the most popular va models using the saliency map generated by the best va model the proposed texture regularity metric is computed it is shown through subjective testing that the proposed metric has a strong correlation with the mean opinion score for the perceived regularity of textures the proposed texture regularity metric can be used to improve the quality and performance of many image processing applications like texture synthesis texture compression and content based image retrieval

maximum likelihood parameter estimation of textures using a wold decomposition based model we present a solution to the problem of modeling parameter estimation and synthesis of natural textures the texture field is assumed to be a realization of a regular homogeneous random field which can have a mixed spectral distribution on the basis of a 2 d wold like decomposition the field is represented as a sum of a purely indeterministic component a harmonic component and a countable number of evanescent fields we present a maximum likelihood solution to the joint parameter estimation problem of these components from a single observed realization of the texture field the proposed solution is a twostage algorithm in the first stage we obtain an estimate for the number of harmonic and evanescent components in the field and a suboptimal initial estiqate for the parameters of their spectral supports in the second stage we refine these initial estimates by iterative maximization of the likelihood function of the observed data by introducing appropriate parameter transformations the highly nonlinear least squares problem that results from the maximization of the likelihood function is transformed into a separable least squares problem in this new problem the solution for the unknown spectral supports of the harmonic and evanescent components reduces the problem of solving for the transformed parameters of the field to linear least squares solution of the transformation equations then provides a complete solution of the field model parameter estimation problem the wold based model and the resulting analysis and synthesis algorithms are seen applicable to a wide variety of texture types found in natural images the support or shape of the analyzed texture patch may be arbitrary our model is very efficient in terms of the number of parameters required to represent and faithfully reconstruct the original texture

complete canonical correlation analysis with application to multi view gait recognition canonical correlation analysis cca is a well known multivariate analysis method for quantifying the correlations between two sets of multidimensional variables however for multi view gait recognition it is difficult to directly apply cca to deal with two sets of high dimensional vectors because of computational complexity moreover in such situation the eigenmatrix of cca is usually singular which makes the direct implementation of the cca algorithm almost impossible in practice pca or singular value decomposition is employed as a preprocessing step to solve these problems nevertheless this strategy may discard dimensions that contain important discriminative information and correlation information to overcome the shortcomings of cca when dealing with two sets of high dimensional vectors we develop a novel method named complete canonical correlation analysis c3a in our method we first reformulate the traditional cca so that we can avoid the computing of the inverse of a high dimensional matrix with the help of this reformulation c3a further transforms the singular generalized eigensystem computation of cca into two stable eigenvalue decomposition problems moreover a feasible and effective method is proposed to alleviate the computational burden of high dimensional matrix for typical gait image data experimental results on two benchmark gait databases casia gait database and the challenge usf gait database demonstrate the effectiveness of the proposed method highlightswe overcome the shortcomings of cca when dealing with high dimensional matrix the singularity of generalized eigenvalue problem in cca is overcome naturally the important discriminative information is preserved completely in our algorithm our scheme learns stable and complete solutions the multi view gait recognition is achieved based on our method

noise robust semi supervised learning via fast sparse coding this paper presents a novel noise robust graph based semi supervised learning algorithm to deal with the challenging problem of semi supervised learning with noisy initial labels inspired by the successful use of sparse coding for noise reduction we choose to give new l1 norm formulation of laplacian regularization for graph based semi supervised learning since our l1 norm laplacian regularization is explicitly defined over the eigenvectors of the normalized laplacian matrix we formulate graph based semi supervised learning as an l1 norm linear reconstruction problem which can be efficiently solved by sparse coding furthermore by working with only a small subset of eigenvectors we develop a fast sparse coding algorithm for our l1 norm semi supervised learning finally we evaluate the proposed algorithm in noise robust image classification the experimental results on several benchmark datasets demonstrate the promising performance of the proposed algorithm highlightswe propose novel semi supervised learning based on fast sparse coding our algorithm achieves promising results in noise robust image classification our algorithm can readily be extended to many other challenging problems

manifold ranking based retrieval using k regular nearest neighbor graph manifold ranking is a powerful method in semi supervised learning and its performance heavily depends on the quality of the constructed graph in this paper we propose a novel graph structure named k regular nearest neighbor k rnn graph as well as its constructing algorithm and apply the new graph structure in the framework of manifold ranking based retrieval we show that the manifold ranking algorithm based on our proposed graph structure performs better than that of the existing graph structures such as k nearest neighbor k nn graph and connected graph in image retrieval 2d data clustering as well as 3d model retrieval in addition the automatic sample reweighting and graph updating algorithms are presented for the relevance feedback of our algorithm experiments demonstrate that the proposed algorithm outperforms the state of the art algorithms

spectral representation and asymptotic properties of certain deterministic fields with innovation components in this paper we derive the spectral and ergodic properties of a special class of homogeneous random fields which includes an important family of evanescent random fields based on a derivation of the resolution of the identity for the operators generating the homogeneous field and using the properties of measurable transformations the spectral representation of both the field and its covariance sequence are derived a necessary and sufficient condition for the existence of such representation is introduced using an analysis approach that employs the solution to the linear diophantine equations further characterization and modeling of the spectral properties of evanescent fields are provided by considering their spectral pseudo density function defined in this paper the geometric properties of the spectral pseudo density of the evanescent field are investigated finally necessary and sufficient conditions for mean and strong ergodicity of the first and second order moments of these fields are derived the analysis initially carried out for complex valued random fields is later extended to include the case of real valued fields

characterization of g regularity for super brownian motion and consequences for parabolic partial differential equations we give a characterization of g regularity for super brownian motion and the brownian snake more precisely we define a capacity on e 0 x r d which is not invariant by translation we then prove that the measure of hitting a borel set a c e for the graph of the brownian snake excursion starting at 0 0 is comparable up to multiplicative constants to its capacity this implies that super brownian motion started at time 0 at the dirac mass 0 hits immediately a i e 0 0 is g regular for a c if and only if its capacity is infinite as a direct consequence if q e is a domain such that 0 0 q we give a necessary and sufficient condition for the existence on q of a positive solution of t u 1 2 u 2u 2 which blows up at 0 0 we also give an estimate of the hitting probabilities for the support of super brownian motion at fixed time we prove that if d 2 the support of super brownian motion is intersection equivalent to the range of brownian motion

superdiffusions and parabolic nonlinear differential equations we establish connections between superdiffusion processes and one class of nonlinear parabolic differential equations analytic results due to brezis and friedman baras and pierre and others are used to investigate the graphs of superdiffusions a survey of the literature and general comments are presented in section 4

a probabilistic approach to one class of nonlinear differential equations we establish connections between positive solutions of one class of nonlinear partial differential equations and hitting probabilities and additive functionals of superdiffusion processes as an application we improve results on superprocesses by using the recent progress in the theory of removable singularities for differential equations

waveguide a joint wavelet based image representation and description system data representation and content description are two basic components required by the management of any image database a wavelet based system called the waveguide which integrates these two components in a unified framework is proposed in this work in the waveguide system images are compressed with the state of the art wavelet coding technique and indexed with color texture and object shape descriptors generated in the wavelet domain during the encoding process all the content descriptors are extracted by machines automatically with a low computational complexity and stored with a low memory space extensive experiments are performed to demonstrate the performance of the new approach

statistical texture characterization from discrete wavelet representations we conjecture that texture can be characterized by the statistics of the wavelet detail coefficients and therefore introduce two feature sets 1 the wavelet histogram signatures which capture all first order statistics using a model based approach and 2 the wavelet co occurrence signatures which reflect the coefficients second order statistics the introduced feature sets outperform the traditionally used energy best performance is achieved by combining histogram and co occurrence signatures

a unified texture model based on a 2 d wold like decomposition a unified texture model that is applicable to a wide variety of texture types found in natural images is presented this model leads to the derivation of texture analysis and synthesis algorithms designed to estimate the texture parameters and to reconstruct the original texture field from these parameters the texture field is assumed to be a realization of a regular homogeneous random field which is characterized in general by a mixed spectral distribution the texture field is orthogonally decomposed into a purely indeterministic component and a deterministic component the deterministic component is further orthogonally decomposed into a harmonic component and a generalized evanescent component both analytical and experimental results show that the deterministic components should be parameterized separately from the purely indeterministic component the model is very efficient in terms of the number of parameters required to faithfully represent textures reconstructed textures are practically indistinguishable from the originals

estimation and choice of neighbors in spatial interaction models of images some aspects of statistical inference for a class of spatial interaction models for finite images are presented primarily the simultaneous autoregressive sar models and conditional markov cm models each of these models is characterized by a set of neighbors a set of coefficients and a noise sequence of specified characteristics we are concerned with two problems the estimation of the unknown parameters in both sar and cm models and the choice of an appropriate model from a class of such competing models assuming gaussian distributed variables we discuss maximum likelihood ml estimation methods in general the ml scheme leads to nonlinear optimization problems to avoid excessive computation an iterative scheme is given for sar models which gives approximate ml estimates in the gaussian case and reasonably good estimates in some non gaussian situations as well likewise for cm models an easily computable consistent estimate is given the asymptotic mean squared error mse of this estimate for a four neighbor cm model is shown tn be substantially less than the mse of the popular coding estimate asymptotically consistent decision rules are given for choosing an appropriate sar or cm model the usefulness of the estimation scheme and the decision rule for the choice of neighbors is illustrated by using synthetic patterns synthetic patterns obeying known sar and cm models are generated and the models corresponding to true and several competing neighbor sets are fitted the estimation scheme yields estimates close to the parameters of the true models and the decision rule for the choice of neighbors picks up the true model from the class of competing models

kolmogorov s test for the brownian snake we present a kolmogorov s test for the brownian snake this result was conjectured by le gall in 1998 it has to be compared with kolmogorov s test for super brownian motion by dhersin and le gall

fingerprint classification using fast fourier transform and nonlinear discriminant analysis in this paper we present a new approach for fingerprint classification based on discrete fourier transform dft and nonlinear discriminant analysis utilizing the dft and directional filters a reliable and efficient directional image is constructed from each fingerprint image and then nonlinear discriminant analysis is applied to the constructed directional images reducing the dimension dramatically and extracting the discriminant features the proposed method explores the capability of dft and directional filtering in dealing with low quality images and the effectiveness of nonlinear feature extraction method in fingerprint classification experimental results demonstrates competitive performance compared with other published results

canonical correlation analysis for multilabel classification a least squares formulation extensions and analysis canonical correlation analysis cca is a well known technique for finding the correlations between two sets of multidimensional variables it projects both sets of variables onto a lower dimensional space in which they are maximally correlated cca is commonly applied for supervised dimensionality reduction in which the two sets of variables are derived from the data and the class labels respectively it is well known that cca can be formulated as a least squares problem in the binary class case however the extension to the more general setting remains unclear in this paper we show that under a mild condition which tends to hold for high dimensional data cca in the multilabel case can be formulated as a least squares problem based on this equivalence relationship efficient algorithms for solving least squares problems can be applied to scale cca to very large data sets in addition we propose several cca extensions including the sparse cca formulation based on the 1 norm regularization we further extend the least squares formulation to partial least squares in addition we show that the cca projection for one set of variables is independent of the regularization on the other set of multidimensional variables providing new insights on the effect of regularization on cca we have conducted experiments using benchmark data sets experiments on multilabel data sets confirm the established equivalence relationships results also demonstrate the effectiveness and efficiency of the proposed cca extensions

facial expression recognition using kernel canonical correlation analysis kcca in this correspondence we address the facial expression recognition problem using kernel canonical correlation analysis kcca following the method proposed by lyons et al and zhang et al we manually locate 34 landmark points from each facial image and then convert these geometric points into a labeled graph lg vector using the gabor wavelet transformation method to represent the facial features on the other hand for each training facial image the semantic ratings describing the basic expressions are combined into a six dimensional semantic expression vector learning the correlation between the lg vector and the semantic expression vector is performed by kcca according to this correlation we estimate the associated semantic expression vector of a given test image and then perform the expression classification according to this estimated semantic expression vector moreover we also propose an improved kcca algorithm to tackle the singularity problem of the gram matrix the experimental results on the japanese female facial expression database and the ekman s pictures of facial affect database illustrate the effectiveness of the proposed method

a theory for multiresolution signal decomposition the wavelet representation multiresolution representations are effective for analyzing the information content of images the properties of the operator which approximates a signal at a given resolution were studied it is shown that the difference of information between the approximation of a signal at the resolutions 2 sup j 1 and 2 sup j where j is an integer can be extracted by decomposing this signal on a wavelet orthonormal basis of l sup 2 r sup n the vector space of measurable square integrable n dimensional functions in l sup 2 r a wavelet orthonormal basis is a family of functions which is built by dilating and translating a unique function psi x this decomposition defines an orthogonal multiresolution representation called a wavelet representation it is computed with a pyramidal algorithm based on convolutions with quadrature mirror filters wavelet representation lies between the spatial and fourier domains for images the wavelet representation differentiates several spatial orientations the application of this representation to data compression in image coding texture discrimination and fractal analysis is discussed