label propagation and higher order constraint based segmentation of fluid associated regions in retinal sd oct images the segmentation of the fluid associated region in the retina plays an important role in the treatment of retinal diseases such as diabetic retinopathy dr and age related macular degeneration amd the existing methods for the detection of the fluid region generally suffer from the limitation of the segmentation accuracy and the expensive time costs to overcome these problems in this paper we propose an interactive segmentation method for the fluid associated region in three dimensional 3 d spectral domain optical coherence tomography sd oct retinal imaging in which only a few seeds in one sd oct slice are needed after which the algorithm finishes the segmentation automatically to improve the segmentation accuracy the higher order constraint is introduced into the conventional markov random field mrf framework to impose the superpixel consistency to maintain temporal coherence of the 3 d sd oct slices the labeling information is propagated slice by slice via a proposed motion estimation based algorithm the proposed higher order based energy function can be efficiently solved by the max flow algorithm on a specified graph with several auxiliary nodes experiments on 28 sd oct cubes demonstrate the competitiveness of the proposed method compared with the state of the art methods

bayesian approach with hidden markov modeling and mean field approximation for hyperspectral data analysis the main problems in hyperspectral image analysis are spectral classification segmentation and data reduction in this paper we propose a bayesian estimation approach which gives a joint solution for these problems the problem is modeled as a blind sources separation bss the data are m hyperspectral images and the sources are k m images which are composed of compact homogeneous regions and have mutually disjoint supports the set of all these regions cover the total surface of the observed scene to insure these properties we propose a hierarchical markov model for the sources with a common hidden classification field which is modeled via a potts markov field the joint bayesian estimation of the hidden variable sources and the mixing matrix of the bss gives a solution for all three problems spectra classification segmentation and data reduction of hyperspectral images the mean field approximation mfa algorithm for the posterior laws is proposed for the effective bayesian computation finally some results of the application of the proposed methods on simulated and real data are given to illustrate the performance of the proposed method compared to other classical methods such as pca and ica

semi supervised lpp algorithms for learning to rank based visual search reranking two feature dimensionality reduction methods for search reranking are proposed the sslpp algorithm improves lpp by incorporating the relevance degree information the proposed pcc sslpp algorithm models the correlation information into sslpp the proposed methods achieve better ndcg performance recent years have witnessed the success of learning to rank technique in visual search reranking generally dimensionality reduction is one of the important stages in this kind of method to deal with the high dimensional visual features however these methods are typically designed for classification and retrieval applications rather than ranking direct utilization of them cannot guarantee the best performance in ranking applications as ranking information e g relevance degree label is absent to solve this problem a semi supervised lpp algorithm called sslpp is proposed the method incorporates the relevance degree information into a manifold dimensionality reduction method i e locality preserving projections lpp to accurately preserve the correlation information between two images in the low intrinsic dimensional space a pearson correlation coefficient pcc based sslpp algorithm called pcc sslpp is further developed the pcc sslpp method models the correlation information between two images in the graph construction step of sslpp besides a novel visual search reranking scheme is also presented based on the proposed sslpp pcc sslpp and learning to rank experiments on two popular real world image reranking datasets demonstrate the effectiveness of the proposed scheme and algorithms in visual search reranking

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

blind source separation by nonstationarity of variance a cumulant based approach blind separation of source signals usually relies either on the nongaussianity of the signals or on their linear autocorrelations a third approach was introduced by matsuoka et al 1995 who showed that source separation can be performed by using the nonstationarity of the signals in particular the nonstationarity of their variances in this paper we show how to interpret the nonstationarity due to a smoothly changing variance in terms of higher order cross cumulants this is based on the time correlation of the squares energies of the signals and leads to a simple optimization criterion using this criterion we construct a fixed point algorithm that is computationally very efficient

joint distance and similarity measure learning based on triplet based constraints a novel model is proposed to learn combined distance and similarity measure cdsm based on triplet based constraints our cdsm can be kernelized for learning nonlinear measures two effective strategies are suggested to speed up the training and testing of kernelized cdsm extensive experiments validate the superiority of cdsm and kernelized cdsm distance and similarity measures usually are complementary to pattern classification with pairwise constraints several approaches have been proposed to combine distance and similarity measures however it remains less investigated to use triplets of samples for joint learning of distance and similarity measures moreover the kernel extension of triplet based model is also nontrivial and computationally expensive in this paper we propose a novel method to learn a combined distance and similarity measure cdsm by incorporating with the max margin model we suggest a triplet based cdsm learning model with a unified regularizer of the frobenius norm a support vector machine svm based algorithm is then adopted to solve the optimization problem furthermore we extend cdsm for learning nonlinear measures via the kernel trick two effective strategies are adopted to speed up training and testing of kernelized cdsm experiments on the uci handwritten digits and person re identification datasets demonstrate that cdsm and kernelized cdsm outperform several state of the art metric learning methods

linear neighborhood propagation and its applications in this paper a novel graph based transductive classification approach called linear neighborhood propagation is proposed the basic idea is to predict the label of a data point according to its neighbors in a linear way this method can be cast into a second order intrinsic gaussian markov random field framework its result corresponds to a solution to an approximate inhomogeneous biharmonic equation with dirichlet boundary conditions different from existing approaches our approach provides a novel graph structure construction method by introducing multiple wise edges instead of pairwise edges and presents an effective scheme to estimate the weights for such multiple wise edges to the best of our knowledge these two contributions are novel for semi supervised classification the experimental results on image segmentation and transductive classification demonstrate the effectiveness and efficiency of the proposed approach

optimal design of nonlinear phase fir filters with prescribed phase error constrained least squares design and constrained chebyshev design of one and two dimensional nonlinear phase fir filters with prescribed phase error are considered in this paper by a unified semi infinite positive definite quadratic programming approach in order to obtain unique optimal solutions we propose to impose constraints on the complex approximation error and the phase error by introducing a sigmoid phase error constraint bound function the group delay error can be greatly reduced a goldfarb idnani based algorithm is presented to solve the semi infinite positive definite quadratic program resulting from the constrained least squares design problem and then applied after some modifications to the constrained chebyshev design problem which is proved in this paper to be equivalent also to a semi infinite positive definite quadratic program through design examples the proposed method is compared with several existing methods simulation results demonstrate the effectiveness and efficiency of the proposed method

principal manifolds and nonlinear dimensionality reduction via tangent space alignment we present a new algorithm for manifold learning and nonlinear dimensionality reduction based on a set of unorganized data points sampled with noise from a parameterized manifold the local geometry of the manifold is learned by constructing an approximation for the tangent space at each data point and those tangent spaces are then aligned to give the global coordinates of the data points with respect to the underlying manifold we also present an error analysis of our algorithm showing that reconstruction errors can be quite small in some cases we illustrate our algorithm using curves and surfaces both in two dimensional three dimensional 2d 3d euclidean spaces and in higher dimensional euclidean spaces we also address several theoretical and algorithmic issues for further research and improvements

pairwise constraints based multiview features fusion for scene classification recently we have witnessed a surge of interests of learning a low dimensional subspace for scene classification the existing methods do not perform well since they do not consider scenes multiple features from different views in low dimensional subspace construction in this paper we describe scene images by finding a group of features and explore their complementary characteristics we consider the problem of multiview dimensionality reduction by learning a unified low dimensional subspace to effectively fuse these features the new proposed method takes both intraclass and interclass geometries into consideration as a result the discriminability is effectively preserved because it takes into account neighboring samples which have different labels due to the semantic gap the fusion of multiview features still cannot achieve excellent performance of scene classification in real applications therefore a user labeling procedure is introduced in our approach initially a query image is provided by the user and a group of images are retrieved by a search engine after that users label some images in the retrieved set as relevant or irrelevant with the query the must links are constructed between the relevant images and the cannot links are built between the irrelevant images finally an alternating optimization procedure is adopted to integrate the complementary nature of different views with the user labeling information and develop a novel multiview dimensionality reduction method for scene classification experiments are conducted on the real world datasets of natural scenes and indoor scenes and the results demonstrate that the proposed method has the best performance in scene classification in addition the proposed method can be applied to other classification problems the experimental results of shape classification on caltech 256 suggest the effectiveness of our method highlights we describe scene images through multimodal features we explore the complementary characteristics of these features we address multimodal dimensionality reduction by learning a unified low dimensional subspace we adopt alternating optimization to integrate the features with the user labeling information we develop a novel multimodal dimensionality reduction method for scene classification

boosted random contextual semantic space based representation for visual recognition we propose an image representation method using boosted random contextual semantic spaces the proposed brcss can alleviate the semantic gap by using semantic space based image representation brcss uses the visual representation of images in an iterative way with random sampling and re weighting experimental results demonstrate the effectiveness and efficiency of the proposed method visual information has been widely used for image representation although proven very effective the visual representation lacks explicit semantics however how to generate a proper semantic space for image representation is still an open problem that needs to be solved to jointly model the visual and semantic representations of images we propose a boosted random contextual semantic space based image representation method images are initially represented using local feature s distribution histograms the semantic space is generated by randomly selecting training images images are then mapped into the semantic space accordingly semantic context is explored to model the correlations of different semantics which is then used for classification the classification results are used to re weight training images in a boosted way the re weighted images are used to construct new semantic space for classification in this way we are able to jointly consider the visual and semantic information of images image classification experiments on several public datasets show the effectiveness of the proposed method

normalized cuts and image segmentation we propose a novel approach for solving the perceptual grouping problem in vision rather than focusing on local features and their consistencies in the image data our approach aims at extracting the global impression of an image we treat image segmentation as a graph partitioning problem and propose a novel global criterion the normalized cut for segmenting the graph the normalized cut criterion measures both the total dissimilarity between the different groups as well as the total similarity within the groups we show that an efficient computational technique based on a generalized eigenvalue problem can be used to optimize this criterion we applied this approach to segmenting static images as well as motion sequences and found the results to be very encouraging

learning a mahalanobis distance metric for data clustering and classification distance metric is a key issue in many machine learning algorithms this paper considers a general problem of learning from pairwise constraints in the form of must links and cannot links as one kind of side information a must link indicates the pair of the two data points must be in a same class while a cannot link indicates that the two data points must be in two different classes given must link and cannot link information our goal is to learn a mahalanobis distance metric under this metric we hope the distances of point pairs in must links are as small as possible and those of point pairs in cannot links are as large as possible this task is formulated as a constrained optimization problem in which the global optimum can be obtained effectively and efficiently finally some applications in data clustering interactive natural image segmentation and face pose estimation are given in this paper experimental results illustrate the effectiveness of our algorithm

automatic image annotation by semi supervised manifold kernel density estimation the insufficiency of labeled training data is a major obstacle in automatic image annotation to tackle this problem we propose a semi supervised manifold kernel density estimation ssmkde approach based on a recently proposed manifold kde method our contributions are twofold first ssmkde leverages both labeled and unlabeled samples and formulates all data in a manifold structure which enables a more accurate label prediction second the relationship between kde based methods and graph based semi supervised learning ssl methods is analyzed which helps to better understand graph based ssl methods extensive experiments demonstrate the superiority of ssmkde over existing kde based and graph based ssl methods

image classification by search with explicitly and implicitly semantic representations we model the explicitly and implicitly semantic representations for image classification explicitly semantic representations are obtained using learned models with supervision implicitly semantic representations are obtained with information from the internet the explicitly and implicitly semantic based method achieves good performances image classification refers to the task of automatically classifying the categories of images based on the contents this task is typically solved using visual features with the histogram based classification scheme although effective this strategy has two drawbacks on one hand histogram based representation often disregards the object layout which is very important for classification on the other hand visual features are unable to fully separate different images due to the semantic gap to solve these two problems in this paper we propose a novel image classification method by explicitly and implicitly representing the images with searching strategy first to make use of object layouts we randomly select a number of regions and then use these regions for image representations second we generate the explicitly semantic representations using a number of pre learned semantic models third we measure the visual similarities with the internet images and use the text information for implicitly semantic representations since internet images are contaminated with noise the resulting representations only implicitly reflect the contents of images finally both the explicitly and implicitly semantic representations are jointly modeled for image classifications by training bi linear classifiers we evaluate the effectiveness of the proposed image classification by search with explicitly and implicitly semantic representations method eisr on the scene 15 dataset the mit indoor dataset the uiuc sports dataset and the pascal voc 2007 dataset the experimental results prove the usefulness of the proposed method

a hyperspectral image classification framework and its application a new descriptor called lrp is proposed to describe subtle texture for 2d image texture enhancement is built to extend lrp to represent hsi a supervised manifold learning method called dlpp is proposed to reduce dimension the proposed framework outperforms other methods with experiments classification is a critical step to make full use of the hyperspectral data the most current approaches perform well for analyzing the macro texture but they often fail to deal with the micro texture thus this study proposes a general framework for the material with micro texture based on hyperspectral image hsi technique in this framework local response pattern lrp is firstly proposed to describe 2d image texture to preserve more structural information and keep less sensitive to image conditions then lrp is extended to represent hsi with texture enhancement te by considering opponent relationships between pairs of bands after that discriminated locality preserving projection dlpp is proposed to reduce data dimension in a linearizing nonlinear manifold way finally experiments on the hyperspectral images of fresh and frozen thawed fish fillets are conducted the results demonstrate that the proposed framework is efficient in terms of both recognition rates and robustness

texture recognition based on diffusion in networks a texture recognition method is proposed the image is modeled into networks the texture is characterized by the diffusion over the networks it is demonstrated that directed networks are better to texture recognition the proposed method outperform the state of the art much work has been done in the field of texture analysis and classification while promising classification methods have been proposed most of them rely on classical image analysis approaches this paper presents a texture classification method based on diffusion in directed networks first an image is modeled as a directed network by mapping each pixel as a node and connecting two nodes up to a maximum distance r to reveal texture properties links between two nodes are removed based on the pixel intensity difference once such a network is obtained the activity of each node is estimated by random walks and combined into a histogram to describe the image the main contribution of this paper is the use of directed networks which tends to provide better performance than in undirected cases also we have shown that the activity induced on these networks can be effectively used as texture descriptor experimental results show that the proposed method is favorably compared to traditional texture methods on widely used texture datasets the proposed method is also found to be promising for plant species classification using samples of leaf texture

semantic preserving distance metric learning and applications how do we accurately browse a large set of images or efficiently annotate the images from an image library image clustering methods are invaluable tools for applications such as content based image retrieval and image annotation to perform these tasks it is critical to have proper features to describe the visual and semantic content of images and to define an accurate distance metric to measure the dissimilarity between any two images however existing methods which adopt the features of color histograms edge direction histograms and shape context lack the ability to describe semantic content to solve this problem we propose a new approach that utilizes user provided pairwise constraints to describe the semantic relationship between two images a semantic preserving distance metric learning sp dml algorithm is developed to explore the complementary characteristics of the visual features and pairwise constraints in a unified feature space in this space the learned distance metric can be used to measure the dissimilarity between two images specifically the manifold structure adopted in sp dml is revealed by the image s visual features to integrate semantic contents in distance metric learning sp dml utilizes pairwise constraints to build semantic patches and align these patches to obtain the optimal distance metric for the new feature space experimental results in image clustering demonstrate that the performance of sp dml is appealing

stochastic relaxation gibbs distributions and the bayesian restoration of images we make an analogy between images and statistical mechanics systems pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a lattice like physical system the assignment of an energy function in the physical system determines its gibbs distribution because of the gibbs distribution markov random field mrf equivalence this assignment also determines an mrf image model the energy function is a more convenient and natural mechanism for embodying picture attributes than are the local characteristics of the mrf for a range of degradation mechanisms including blurring nonlinear deformations and multiplicative or additive noise the posterior distribution is an mrf with a structure akin to the image model by the analogy the posterior distribution defines another imaginary physical system gradual temperature reduction in the physical system isolates low energy states annealing or what is the same thing the most probable states under the gibbs distribution the analogous operation under the posterior distribution yields the maximum a posteriori map estimate of the image given the degraded observations the result is a highly parallel relaxation algorithm for map estimation we establish convergence properties of the algorithm and we experiment with some simple pictures for which good restorations are obtained at low signal to noise ratios

digital image restoration using spatial interaction models this paper is concerned with developing fast nonrecursive algorithms for the minimum mean squared error restoration of degraded images the degradation is assumed to be due to a space invariant periodic nonseparable known point spread function and additive white noise our basic approach is to represent the images by a class of spatial interaction models namely the simultaneous autoregressve models and the conditional markov models defined on toroidal lattices and develop minimum mean squared error restoration algorithms using these models the restoration algorithms are optimal if the parameters characterizing the interaction models are exactly known however in practice the parameters are estimated from the images by using spatial interaction models we develop restoration algorithms that do not require the availability of the original image or its prototype the specific structure of the underlying lattice enables the implementation of the filters using fast fourier transform fft computations several restoration examples are given

properties and power spectral densities of cp based oqam ofdm systems offset quadrature amplitude modulation ofdm oqam ofdm systems have low sidelobes but require complex receivers for broadband channels to deal with this problem cyclic prefix cp based oqam ofdm systems have been proposed in the literature in this paper we study oqam ofdm and two existing cp based oqam ofdm systems and propose a new cp based system with good spectrum performance we investigate their properties and analyze their power spectral densities psds we show that the continuous time transmitted cp oqam ofdm signal after the cp insertion is equivalent to a truncated signal obtained by inserting cp and cyclic suffix cs to the discrete time input data symbols in the oqam ofdm system which simplifies the calculation of the psd of the transmitted cp oqam ofdm signal and also implies that the psd of the cp oqam ofdm may be affected by the truncation we show that the perfect reconstruction pr condition for the cp oqam ofdm system is actually the same as that for the oqam ofdm system when the length of the prototype filter is not more than the length of the transmitted cp oqam ofdm signal of each block excluding the cp we also compare three cp based oqam ofdm systems with windowed cp ofdm systems numerical results show that the theoretical and simulated psds of the transmitted signals match well cp based oqam ofdm systems perform better than windowed cp ofdm systems considering both the psd and bit error rate ber performances and the newly proposed cp based oqam ofdm system has the best psd performance in the three cp based oqam ofdm systems with an increase of the data rate overhead

bayes smoothing algorithms for segmentation of binary images modeled by markov random fields a new image segmentation algorithm is presented based on recursive bayes smoothing of images modeled by markov random fields and corrupted by independent additive noise the bayes smoothing algorithm yields the a posteriori distribution of the scene value at each pixel given the total noisy image in a recursive way the a posteriori distribution together with a criterion of optimality then determine a bayes estimate of the scene the algorithm presented is an extension of a 1 d bayes smoothing algorithm to 2 d and it gives the optimum bayes estimate for the scene value at each pixel computational concerns in 2 d however necessitate certain simplifying assumptions on the model and approximations on the implementation of the algorithm in particular the scene noiseless image is modeled as a markov mesh random field a special class of markov random fields and the bayes smoothing algorithm is applied on overlapping strips horizontal vertical of the image consisting of several rows columns it is assumed that the signal scene values vector sequence along the strip is a vector markov chain since signal correlation in one of the dimensions is not fully used along the edges of the strip estimates are generated only along the middle sections of the strips the overlapping strips are chosen such that the union of the middle sections of the strips gives the whole image the bayes smoothing algorithm presented here is valid for scene random fields consisting of multilevel discrete or continuous random variables

blind separation of instantaneous mixtures of nonstationary sources most source separation algorithms are based on a model of stationary sources however it is a simple matter to take advantage of possible nonstationarities of the sources to achieve separation this paper develops novel approaches in this direction based on the principles of maximum likelihood and minimum mutual information these principles are exploited by efficient algorithms in both the off line case via a new joint diagonalization procedure and in the on line case via a newton like procedure some experiments showing the good performance of our algorithms and evidencing an interesting feature of our methods are presented their ability to achieve a kind of super efficiency the paper concludes with a discussion contrasting separating methods for non gaussian and nonstationary models and emphasizing that as a matter of fact what makes the algorithms work is strictly speaking not the nonstationarity itself but rather the property that each realization of the source signals has a time varying envelope

analysis and design of ofdm oqam systems based on filterbank theory a discrete time analysis of the orthogonal frequency division multiplex offset qam ofdm oqam multicarrier modulation technique leading to a modulated transmultiplexer is presented the conditions of discrete orthogonality are established with respect to the polyphase components of the ofdm oqam prototype filter which is assumed to be symmetrical and with arbitrary length fast implementation schemes of the ofdm oqam modulator and demodulator are provided which are based on the inverse fast fourier transform non orthogonal prototypes create intersymbol and interchannel interferences isi and ici that in the case of a distortion free transmission are expressed by a closed form expression a large set of design examples is presented for ofdm oqam systems with the number of subcarriers going from four up to 2048 which also allows a comparison between different approaches to get well localized prototypes

adaptive hypergraph learning and its application in image classification recent years have witnessed a surge of interest in graph based transductive image classification existing simple graph based transductive learning methods only model the pairwise relationship of images however and they are sensitive to the radius parameter used in similarity calculation hypergraph learning has been investigated to solve both difficulties it models the high order relationship of samples by using a hyperedge to link multiple samples nevertheless the existing hypergraph learning methods face two problems i e how to generate hyperedges and how to handle a large set of hyperedges this paper proposes an adaptive hypergraph learning method for transductive image classification in our method we generate hyperedges by linking images and their nearest neighbors by varying the size of the neighborhood we are able to generate a set of hyperedges for each image and its visual neighbors our method simultaneously learns the labels of unlabeled images and the weights of hyperedges in this way we can automatically modulate the effects of different hyperedges thorough empirical studies show the effectiveness of our approach when compared with representative baselines

tight weyl heisenberg frames in l sup 2 z tight weyl heisenberg frames in l sup 2 z are the tool for short time fourier analysis in discrete time they are closely related to paraunitary modulated filter banks and are studied here using techniques of the filter bank theory good resolution of short time fourier analysis in the joint time frequency plane is not attainable unless some redundancy is introduced that is the reason for considering overcomplete weyl heisenberg expansions the main result of this correspondence is a complete parameterization of finite length tight weyl heisenberg frames in l sup 2 z with arbitrary rational oversampling ratios this parameterization follows from a factorization of polyphase matrices of paraunitary modulated filter banks which is introduced first

error bounds of multi graph regularized semi supervised classification in this paper we investigate the generalization performance of the multi graph regularized semi supervised classification algorithm associated with the hinge loss we provide estimates for the excess misclassification error of multi graph regularized classifiers and show the relations between the generalization performance and the structural invariants of data graphs experiments performed on real database demonstrate the effectiveness of our theoretical analysis

maximum likelihood blind image separation using nonsymmetrical half plane markov random fields this paper presents a maximum likelihood approach for blindly separating linear instantaneous mixtures of images the spatial autocorrelation within each image is described using nonsymmetrical half plane nshp markov random fields in order to simplify the joint probability density functions of the source images a first implementation assuming stationary sources is presented it is then extended to a more realistic nonstationary image model two approaches respectively based on blocking and kernel smoothing are proposed to cope with the nonstationarity of the images the estimation of the mixing matrix is performed using an iterative equivariant version of the newton raphson algorithm moreover score functions required for the computation of the updating rule are approximated at each iteration by parametric polynomial estimators results achieved with artificial mixtures of both artificial and real world images including an astrophysical application clearly prove the high performance of our methods as compared to classical algorithms

local structure preserving discriminative projections for rgb d sensor based scene classification rgb d sensor based scene classification has recently received attention because of its potential use in human computer interaction applications however when feature representation schemes are adopted the dimensionality of concatenated scene features extracted from the rgb and depth image pair is very high and faces the curse of dimensionality therefore we present a new subspace selection scheme called local structure preserving discriminative projections lspdp lspdp simultaneously considers two issues 1 achieving a balance between the local patch structure and global within class structure and 2 maximizing between class distances extensive experimentation on the nyu depth v1 dataset with feature sets computed using three popular schemes locality constrained linear coding llc spatial pyramid matching using sparse coding sc spm and efficient match kernels emks demonstrates the robustness and effectiveness of the new method for rgb d sensor based scene classification

prototype filter optimization to minimize stopband energy with npr constraint for filter bank multicarrier modulation systems recently filter bank multicarrier fbmc modulations have attracted increasing attention the filter banks of fbmc are derived from a prototype filter that determines the system performance such as stopband attenuation intersymbol interference isi and interchannel interference ici in this paper we formulate a problem of direct optimization of the filter impulse response coefficients for the fbmc systems to minimize the stopband energy and constrain the isi ici unfortunately this filter optimization problem is nonconvex and highly nonlinear nevertheless observing that all the functions in the optimization problem are twice differentiable we propose using the alpha based branch and bound alpha bb algorithm to obtain the optimal solution however the convergence time of the algorithm is unacceptable because the number of unknowns i e the filter coefficients in the optimization problem is too large the main contribution of this paper is that we propose a method to dramatically reduce the number of unknowns of the optimization problem through approximation of the constraints so that the optimal solution of the approximated optimization problem can be obtained with acceptable computational complexity numerical results show that the proposed approximation is reasonable and the optimized filters obtained with the proposed method achieve significantly lower stopband energy than those with the frequency sampling and windowing based techniques

solving partially observable problems with inaccurate psr models modeling dynamical systems is a commonly used technique to solve partially observable problems in the artificial intelligence field predictive state representations psrs have been proposed as an alternative to partially observable markov decision processes pomdps to model dynamical systems although pomdps and psrs provide general frameworks to solve partially observable problems they rely heavily on a known and accurate model of the environment however in real world applications it is extremely difficult to build an accurate model in this paper we propose an algorithm to solve partially observable problems using an inaccurate psr model which is learned from samples the proposed algorithm can also improve the accuracy of the learned model given the inaccurate psr model the psr state is identified firstly then the traditional markov decision processes mdp techniques are used to solve the partially observable problem furthermore the learned model which may get off track as often happens when the model is learned from samples can be reset the effectiveness of our proposed algorithm is demonstrated based on a standard set of pomdp test problems

estimating functions of independent component analysis for temporally correlated signals this article studies a general theory of estimating functions of independent component analysis when the independent source signals are temporarily correlated estimating functions are used for deriving both batch and on line learning algorithms and they are applicable to blind cases where spatial and temporal probability structures of the sources are unknown most algorithms proposed so far can be analyzed in the framework of estimating functions an admissible class of estimating functions is derived and related efficient on line learning algorithms are introduced we analyze dynamical stability and statistical efficiency of these algorithms different from the independently and identically distributed case the algorithms work even when only the second order moments are used the method of simultaneous diagonalization of cross covariance matrices is also studied from the point of view of estimating functions

multi label ensemble based on variable pairwise constraint projection multi label classification has attracted an increasing amount of attention in recent years to this end many algorithms have been developed to classify multi label data in an effective manner however they usually do not consider the pairwise relations indicated by sample labels which actually play important roles in multi label classification inspired by this we naturally extend the traditional pairwise constraints to the multi label scenario via a flexible thresholding scheme moreover to improve the generalization ability of the classifier we adopt a boosting like strategy to construct a multi label ensemble from a group of base classifiers to achieve these goals this paper presents a novel multi label classification framework named variable pairwise constraint projection for multi label ensemble vpcme specifically we take advantage of the variable pairwise constraint projection to learn a lower dimensional data representation which preserves the correlations between samples and labels thereafter the base classifiers are trained in the new data space for the boosting like strategy we employ both the variable pairwise constraints and the bootstrap steps to diversify the base classifiers empirical studies have shown the superiority of the proposed method in comparison with other approaches

exploiting pairwise recommendation and clustering strategies for image re ranking in content based image retrieval cbir systems accurately ranking collection images is of great relevance users are interested in the returned images placed at the first positions which usually are the most relevant ones commonly image content descriptors are used to compute ranked lists in cbir systems in general these systems perform only pairwise image analysis that is compute similarity measures considering only pairs of images ignoring the rich information encoded in the relations among several images this paper presents a novel re ranking approach used to improve the effectiveness of cbir tasks by exploring relations among images in our approach a recommendation based strategy is combined with a clustering method both exploit contextual information encoded in ranked lists computed by cbir systems we conduct several experiments to evaluate the proposed method our experiments consider shape color and texture descriptors and comparisons with other post processing methods experimental results demonstrate the effectiveness of our method

rank minimization with applications to image noise removal abstract rank minimization problem has a wide range of applications in different areas however since this problem is np hard and non convex the frequently used method is to replace the matrix rank minimization with nuclear norm minimization nuclear norm is the convex envelope of the matrix rank and it is more computationally tractable matrix completion is a special case of rank minimization problem in this paper we consider directly using matrix rank as the regularization term instead of nuclear norm in the cost function for matrix completion problem the solution is analyzed and obtained by a hard thresholding operation on the singular values of the observed matrix then by exploiting patch based nonlocal self similarity scheme we apply the proposed rank minimization algorithm to remove white gaussian additive noise in images gamma multiplicative noise is also removed in logarithm domain the experimental results illustrate that the proposed algorithm can remove noises in images more efficiently than nuclear norm can do and the results are also competitive with those obtained by using the existing state of the art noise removal methods in the literature

modeling the shape of the scene a holistic representation of the spatial envelope in this paper we propose a computational model of the recognition of real world scenes that bypasses the segmentation and the processing of individual objects or regions the procedure is based on a very low dimensional representation of the scene that we term the spatial envelope we propose a set of perceptual dimensions naturalness openness roughness expansion ruggedness that represent the dominant spatial structure of a scene then we show that these dimensions may be reliably estimated using spectral and coarsely localized information the model generates a multidimensional space in which scenes sharing membership in semantic categories e g streets highways coasts are projected closed together the performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category

shape matching and object recognition using shape contexts we present a novel approach to measuring similarity between shapes and exploit it for object recognition in our framework the measurement of similarity is preceded by 1 solving for correspondences between points on the two shapes 2 using the correspondences to estimate an aligning transform in order to solve the correspondence problem we attach a descriptor the shape context to each point the shape context at a reference point captures the distribution of the remaining points relative to it thus offering a globally discriminative characterization corresponding points on two similar shapes will have similar shape contexts enabling us to solve for correspondences as an optimal assignment problem given the point correspondences we estimate the transformation that best aligns the two shapes regularized thin plate splines provide a flexible class of transformation maps for this purpose the dissimilarity between the two shapes is computed as a sum of matching errors between corresponding points together with a term measuring the magnitude of the aligning transform we treat recognition in a nearest neighbor classification framework as the problem of finding the stored prototype shape that is maximally similar to that in the image results are presented for silhouettes trademarks handwritten digits and the coil data set

manifold regularized cross modal embedding for zero shot learning zero shot learning zsl aims at classifying previously unseen class samples and has gained its popularity in applications where samples of some categories are scarce for training the basic idea to address this issue is transferring knowledge from the seen classes to the unseen classes through mapping the visual feature to an embedding space spanned by class semantic information the class semantic information can be obtained from human labeled attributes or text corpus in an unsupervised fashion therefore the embedding function from visual space to the embedding space is extremely important however the existing embedding approaches to zsl mainly focus on aligning pairwise semantic consistency from heterogeneous spaces but ignore the intrinsic structure of the locally homogeneous isomorph in order to preserve the locally visual structure in the embedding process this paper proposes a manifold regularized cross modal embedding mcme approach for zsl by formulating the manifold constraint for intrinsic structure of the visual features as well as aligning pairwise consistency the linear closed form solution makes mcme efficient to compute furthermore rather than applying the embedding function learned from the seen classes directly we also propose a new domain adaptation strategy to overcome the domain shift problem during the knowledge transfer process the mcme with the domain adaptation method is called mcme da extensive experiments on the benchmark datasets of awa and cub validate the superiority and promise of mcme and mcme da

distance metric learning for large margin nearest neighbor classification the accuracy of k nearest neighbor knn classification depends significantly on the metric used to compute distances between different examples in this paper we show how to learn a mahalanobis distance metric for knn classification from labeled examples the mahalanobis metric can equivalently be viewed as a global linear transformation of the input space that precedes knn classification using euclidean distances in our approach the metric is trained with the goal that the k nearest neighbors always belong to the same class while examples from different classes are separated by a large margin as in support vector machines svms the margin criterion leads to a convex optimization based on the hinge loss unlike learning in svms however our approach requires no modification or extension for problems in multiway as opposed to binary classification in our framework the mahalanobis distance metric is obtained as the solution to a semidefinite program on several data sets of varying size and difficulty we find that metrics trained in this way lead to significant improvements in knn classification sometimes these results can be further improved by clustering the training examples and learning an individual metric within each cluster we show how to learn and combine these local metrics in a globally integrated manner

blind separation of mixture of independent sources through a quasi maximum likelihood approach we propose two methods for separating mixture of independent sources without any precise knowledge of their probability distribution they are obtained by considering a maximum likelihood ml solution corresponding to some given distributions of the sources and relaxing this assumption afterward the first method is specially adapted to temporally independent non gaussian sources and is based on the use of nonlinear separating functions the second method is specially adapted to correlated sources with distinct spectra and is based on the use of linear separating filters a theoretical analysis of the performance of the methods has been made a simple procedure for optimally choosing the separating functions is proposed further in the second method a simple implementation based on the simultaneous diagonalization of two symmetric matrices is provided finally some numerical and simulation results are given illustrating the performance of the method and the good agreement between the experiments and the theory

time domain channel estimation for oqam ofdm systems algorithms and performance bounds in this paper we first present a general time domain model for the channel estimation in the orthogonal frequency division multiplexing system with offset quadrature amplitude modulation oqam ofdm and utilize the frequency domain pilots to estimate the time domain channel impulse responses different form the conventional methods there is no specific requirement for the length of the symbol interval compared to the the maximum channel delay spread in the proposed scheme furthermore with the proposed time domain model the channel statistic information could be utilized to improve the performance of the channel estimation then we propose two channel estimation schemes i e linear minimum mean square error lmmse and weighted least square wls and we also derive their corresponding bayesian cramer rao bound bcrb and cramer rao bound crb bounds respectively simulation results demonstrate that the bcrb and crb bounds could be achieved by the proposed lmmse and wls methods respectively moreover simulation results show that the proposed methods are much robust to the time synchronization error compared to the conventional frequency domain methods and imply that the pulse shaping filter with waveforms concentrated in the time domain could be employed in oqam ofdm systems to improve the channel estimation performance and spectral efficiency

a blind source separation technique using second order statistics separation of sources consists of recovering a set of signals of which only instantaneous linear mixtures are observed in many situations no a priori information on the mixing matrix is available the linear mixture should be blindly processed this typically occurs in narrowband array processing applications when the array manifold is unknown or distorted this paper introduces a new source separation technique exploiting the time coherence of the source signals in contrast with other previously reported techniques the proposed approach relies only on stationary second order statistics that are based on a joint diagonalization of a set of covariance matrices asymptotic performance analysis of this method is carried out some numerical simulations are provided to illustrate the effectiveness of the proposed method

generalization performance of magnitude preserving semi supervised ranking with graph based regularization semi supervised ranking is a relatively new and important learning problem inspired by many applications we propose a novel graph based regularized algorithm which learns the ranking function in the semi supervised learning framework it can exploit geometry of the data while preserving the magnitude of the preferences the least squares ranking loss is adopted and the optimal solution of our model has an explicit form we establish error analysis of our proposed algorithm and demonstrate the relationship between predictive performance and intrinsic properties of the graph the experiments on three datasets for recommendation task and two quantitative structure activity relationship datasets show that our method is effective and comparable to some other state of the art algorithms for ranking

equivariant adaptive source separation source separation consists of recovering a set of independent signals when only mixtures with unknown coefficients are observed this paper introduces a class of adaptive algorithms for source separation that implements an adaptive version of equivariant estimation and is henceforth called equivariant adaptive separation via independence easi the easi algorithms are based on the idea of serial updating this specific form of matrix updates systematically yields algorithms with a simple structure for both real and complex mixtures most importantly the performance of an easi algorithm does not depend on the mixing matrix in particular convergence rates stability conditions and interference rejection levels depend only on the normalized distributions of the source signals closed form expressions of these quantities are given via an asymptotic performance analysis the theme of equivariance is stressed throughout the paper the source separation problem has an underlying multiplicative structure the parameter space forms a matrix multiplicative group we explore the favorable consequences of this fact on implementation performance and optimization of easi algorithms

laplacian eigenmaps for dimensionality reduction and data representation one of the central problems in machine learning and pattern recognition is to develop appropriate representations for complex data we consider the problem of constructing a representation for data lying on a low dimensional manifold embedded in a high dimensional space drawing on the correspondence between the graph laplacian the laplace beltrami operator on the manifold and the connections to the heat equation we propose a geometrically motivated algorithm for representing the high dimensional data the algorithm provides a computationally efficient approach to nonlinear dimensionality reduction that has locality preserving properties and a natural connection to clustering some potential applications and illustrative examples are discussed

image level classification by hierarchical structure learning with visual and semantic similarities abstract image classification methods often use class level information without considering the distinctive character of each image images of the same class may have varied appearances besides visually similar images may not be semantically correlated to solve these problems in this paper we propose a novel image classification method by automatically learning the image level hierarchical structure ilhs using both visual and semantic similarities we try to generate new representations by exploring both visual and semantic similarities of images images are clustered hierarchically to explore their correlations we then use them for image representations the diversity of image classes within each cluster is used to re weight visual similarities the re weighted similarities are aggregated to generate new image representations we conduct image classification experiments on the caltech 256 dataset the pascal voc 2007 dataset and the pascal voc 2012 dataset experimental results demonstrate the effectiveness of the proposed method

distance metrics for high dimensional nearest neighborhood recovery compression and normalization previous work has shown that the minkowski p distance metrics are unsuitable for clustering very high dimensional document data we extend this work we frame statistical theory on the relationships between the euclidean cosine and correlation distance metrics in terms of item neighborhoods we discuss the differences between the cosine and correlation distance metrics and illustrate our discussion with an example from collaborative filtering we introduce a family of normalized minkowski metrics and test their use on both document data and synthetic data generated from the uniform distribution we describe a range of criteria for testing neighborhood homogeneity relative to underlying latent classes we discuss how these criteria are explicitly and implicitly linked to classification performance by testing both normalized and non normalized minkowski p metrics for multiple values of p we separate out distance compression effects from normalization effects for multi class classification problems we believe that distance compression on high dimensional data aids classification and data analysis for document data we find that the cosine and normalized euclidean correlation and proportioned city block metrics give strong neighborhood recovery the proportioned city block metric gives particularly good results for nearest neighbors recovery and should be used when utilizing document data analysis techniques for which nearest neighborhood recovery is important for data generated from the uniform distribution neighborhood recovery improves as the value of p increases

sequential quadratic programming method for solution of electromagnetic inverse problems in this paper a new algorithm namely a reduced hessian sequential quadratic programming sqp method for solving electromagnetic inverse problems is proposed the electromagnetic inverse problem is considered to be a constrained nonlinear programming the reduced hessian sqp method finds the solution of this constrained nonlinear programming by solving a sequential of quadratic programming subproblems the reduced hessian scheme is applied to reduce the requirement of computational memory of the basic sqp method for large inverse problems numerical results are presented to demonstrate the efficiency and accuracy of the proposed method and some comparisons show that the proposed method has a better convergence and a faster speed than the previous methods

tensor rank preserving discriminant analysis for facial recognition facial recognition one of the basic topics in computer vision and pattern recognition has received substantial attention in recent years however for those traditional facial recognition algorithms the facial images are reshaped to a long vector thereby losing part of the original spatial constraints of each pixel in this paper a new tensor based feature extraction algorithm termed tensor rank preserving discriminant analysis trpda for facial image recognition is proposed the proposed method involves two stages in the first stage the low dimensional tensor subspace of the original input tensor samples was obtained in the second stage discriminative locality alignment was utilized to obtain the ultimate vector feature representation for subsequent facial recognition on the one hand the proposed trpda algorithm fully utilizes the natural structure of the input samples and it applies an optimization criterion that can directly handle the tensor spectral analysis problem thereby decreasing the computation cost compared those traditional tensor based feature selection algorithms on the other hand the proposed trpda algorithm extracts feature by finding a tensor subspace that preserves most of the rank order information of the intra class input samples experiments on the three facial databases are performed here to determine the effectiveness of the proposed trpda algorithm

markovian source separation a maximum likelihood ml approach is used to separate the instantaneous mixtures of temporally correlated independent sources with neither preliminary transformation nor a priori assumption about the probability distribution of the sources a markov model is used to represent the joint probability density of successive samples of each source the joint probability density functions are estimated from the observations using a kernel method for the special case of autoregressive models the theoretical performance of the algorithm is computed and compared with the performance of second order algorithms and i i d based separation algorithms

what does the retina know about natural scenes by examining the experimental data on the statistical properties of natural scenes together with retinal contrast sensitivity data we arrive at a first principle theoretical hypothesis for the purpose of retinal processing and its relationship to an animal s environment we argue that the retinal goal is to transform the visual input as much as possible into a statistically independent basis as the first step in creating a redundancy reduced representation in the cortex as suggested by barlow the extent of this whitening of the input is limited however by the need to suppress input noise our explicit theoretical solutions for the retinal filters also show a simple dependence on mean stimulus luminance they predict an approximate weber law at low spatial frequencies and a de vries rose law at high frequencies assuming that the dominant source of noise is quantum we generate a family of contrast sensitivity curves as a function of mean luminance this family is compared to psychophysical data

a markov model for blind image separation by a mean field em algorithm this paper deals with blind separation of images from noisy linear mixtures with unknown coefficients formulated as a bayesian estimation problem this is a flexible framework where any kind of prior knowledge about the source images and the mixing matrix can be accounted for in particular we describe local correlation within the individual images through the use of markov random field mrf image models these are naturally suited to express the joint pdf of the sources in a factorized form so that the statistical independence requirements of most independent component analysis approaches to blind source separation are retained our model also includes edge variables to preserve intensity discontinuities mrf models have been proved to be very efficient in many visual reconstruction problems such as blind image restoration and allow separation and edge detection to be performed simultaneously we propose an expectation maximization algorithm with the mean field approximation to derive a procedure for estimating the mixing matrix the sources and their edge maps we tested this procedure on both synthetic and real images in the fully blind case i e no prior information on mixing is exploited and found that a source model accounting for local autocorrelation is able to increase robustness against noise even space variant furthermore when the model closely fits the source characteristics independence is no longer a strict requirement and cross correlated sources can be separated as well

two stage multiple kernel learning for supervised dimensionality reduction in supervised dimensionality reduction methods for pattern recognition tasks the information of the class labels is considered through the process of reducing the input dimensionality to improve the classification accuracy using nonlinear mappings for this purpose makes these models more appropriate for nonlinearly distributed data in this paper a new nonlinear supervised dimensionality reduction model is introduced the dimensionality reduction process in this model is performed through a multiple kernel learning paradigm in two stages in the first stage three suitable criteria for supervised dimensionality reduction containing fisher homoscedasticity and between class distance criteria are used to find the kernel weights with these weights a linear combination of several valid kernels is computed to make a new suitable kernel function in the second stage the kernel discriminant analysis method is employed for nonlinear supervised dimensionality reduction using the kernel computed in the first stage many experiments on a variety of real world datasets including handwritten digits images objects images and other datasets show that the proposed approach among a number of well known related techniques results in accurate and fast classifications a novel approach for non linear supervised dimensionality reduction is proposed the proposed method uses kernel discriminant analysis method with new kernels a multiple kernel learning mkl paradigm produces the new kernel function proper criteria for supervised dimensionality reduction are optimized in the mkl part the proposed method is successfully evaluated using many real world datasets

multimodal graph based reranking for web image search this paper introduces a web image search reranking approach that explores multiple modalities in a graph based learning scheme different from the conventional methods that usually adopt a single modality or integrate multiple modalities into a long feature vector our approach can effectively integrate the learning of relevance scores weights of modalities and the distance metric and its scaling for each modality into a unified scheme in this way the effects of different modalities can be adaptively modulated and better reranking performance can be achieved we conduct experiments on a large dataset that contains more than 1000 queries and 1 million images to evaluate our approach experimental results demonstrate that the proposed reranking approach is more robust than using each individual modality and it also performs better than many existing methods

semisupervised multiview distance metric learning for cartoon synthesis in image processing cartoon character classification retrieval and synthesis are critical so that cartoonists can effectively and efficiently make cartoons by reusing existing cartoon data to successfully achieve these tasks it is essential to extract visual features that comprehensively represent cartoon characters and to construct an accurate distance metric to precisely measure the dissimilarities between cartoon characters in this paper we introduce three visual features color histogram shape context and skeleton to characterize the color shape and action respectively of a cartoon character these three features are complementary to each other and each feature set is regarded as a single view however it is improper to concatenate these three features into a long vector because they have different physical properties and simply concatenating them into a high dimensional feature vector will suffer from the so called curse of dimensionality hence we propose a semisupervised multiview distance metric learning ssm dml ssm dml learns the multiview distance metrics from multiple feature sets and from the labels of unlabeled cartoon characters simultaneously under the umbrella of graph based semisupervised learning ssm dml discovers complementary characteristics of different feature sets through an alternating optimization based iterative algorithm therefore ssm dml can simultaneously accomplish cartoon character classification and dissimilarity measurement on the basis of ssm dml we develop a novel system that composes the modules of multiview cartoon character classification multiview graph based cartoon synthesis and multiview retrieval based cartoon synthesis experimental evaluations based on the three modules suggest the effectiveness of ssm dml in cartoon applications

ordinal margin metric learning and its extension for cross distribution image data propose a novel ordinal margin metric learning ormml by separating the data with large margins and making them distributed in order extend ormml to cope with cross distribution application scenarios named cd ormml extensively demonstrate the superiority of the proposed metric learning methods to related state of the art methods in machine learning and computer vision fields a wide range of applications such as human age estimation and head pose recognition are related to ordinal data in which there exists an order relationship to perform such ordinal estimations in a desired metric space in this paper we first propose a novel ordinal margin metric learning ormml method by separating the data classes with a sequence of margins which makes the classes distribute orderly in the learned metric space then to cope with more realistic scenarios where the data are sampled with each class across multiple distributions we present a cross distribution variant of ormml coined as cd ormml by maximizing the correlation between distributions within each class when conducting metric learning finally extensive experiments on synthetic and publicly available image datasets demonstrate the superiority of the proposed methods in performance to the state of the art methods