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IEEE TRANSACTIONS ON IMAGE PROCESSING 2013年第2期22卷 423-434页

作者： Gao, Shenghua Tsang, Ivor Wai-Hung Chia, Liang-Tien Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Adv Digital Sci Ctr Singapore 639798 Singapore

Recent research has shown the initial success of sparse coding (Sc) in solving many computer vision tasks. Motivated by the fact that kernel trick can capture the non-linear similarity of features, which helps in finding a sparse representation of nonlinear features, we propose kernel sparse representation (KSR). Essentially, KSR is a sparse coding technique in a high dimensional feature space mapped by an implicit mapping function. We apply KSR to feature coding in image classification, face recognition, and kernel matrix approximation. More specifically, by incorporating KSR into spatial pyramid matching (SPM), we develop KSRSPM, which achieves a good performance for image classification. Moreover, KSR-based feature coding can be shown as a generalization of efficient match kernel and an extension of Sc-based SPM. We further show that our proposed KSR using a histogram intersection kernel (HIK) can be considered a soft assignment extension of HIK-based feature quantization in the feature coding process. Besides feature coding, comparing with sparse coding, KSR can learn more discriminative sparse codes and achieve higher accuracy for face recognition. Moreover, KSR can also be applied to kernel matrix approximation in large scale learning tasks, and it demonstrates its robustness to kernel matrix approximation, especially when a small fraction of the data is used. Extensive experimental results demonstrate promising results of KSR in image classification, face recognition, and kernel matrix approximation. All these applications prove the effectiveness of KSR in computer vision and machine learning tasks.

关键词： Face recognition image classification kernel matrix approximation kernel sparse representation nonlinear mapping sparse coding

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Image Transformation Based on Learning Dictionaries across Image Spaces

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2013年第2期35卷 367-380页

作者： Jia, Kui Wang, Xiaogang Tang, Xiaoou Univ Illinois Adv Digital Sci Ctr Urbana IL 61801 USA Chinese Univ Hong Kong Dept Elect Engn Shatin Hong Kong Peoples R China Chinese Univ Hong Kong Dept Informat Engn Shatin Hong Kong Peoples R China

In this paper, we propose a framework of transforming images from a source image space to a target image space, based on learning coupled dictionaries from a training set of paired images. The framework can be used for applications such as image super-resolution and estimation of image intrinsic components (shading and albedo). It is based on a local parametric regression approach, using sparse feature representations over learned coupled dictionaries across the source and target image spaces. After coupled dictionary learning, sparse coefficient vectors of training image patch pairs are partitioned into easily retrievable local clusters. For any test image patch, we can fast index into its closest local cluster and perform a local parametric regression between the learned sparse feature spaces. The obtained sparse representation (together with the learned target space dictionary) provides multiple constraints for each pixel of the target image to be estimated. The final target image is reconstructed based on these constraints. The contributions of our proposed framework are three-fold. 1) We propose a concept of coupled dictionary learning based on coupled sparse coding which requires the sparse coefficient vectors of a pair of corresponding source and target image patches to have the same support, i.e., the same indices of nonzero elements. 2) We devise a space partitioning scheme to divide the high-dimensional but sparse feature space into local clusters. The partitioning facilitates extremely fast retrieval of closest local clusters for query patches. 3) Benefiting from sparse feature-based image transformation, our method is more robust to corrupted input data, and can be considered as a simultaneous image restoration and transformation process. Experiments on intrinsic image estimation and super-resolution demonstrate the effectiveness and efficiency of our proposed method.

关键词： Image transformation image mapping sparse coding intrinsic images super-resolution

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Integration of multi-feature fusion and dictionary learning for face recognition

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IMAGE AND VISION COMPUTING 2013年第12期31卷 895-904页

作者： Wang, Donghui Wang, Xikui Kong, Shu Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Peoples R China

Recent research emphasizes more on analyzing multiple features to improve face recognition (FR) performance. One popular scheme is to extend the sparse representation based classification framework with various sparse constraints. Although these methods jointly study multiple features through the constraints, they just process each feature individually such that they overlook the possible high-level relationship among different features. It is reasonable to assume that the low-level features of facial images, such as edge information and smoothed/ low-frequency image, can be fused into a more compact and more discriminative representation based on the latent high-level relationship. FR on the fused features is anticipated to produce better performance than that on the original features, since they provide more favorable properties. Focusing on this, we propose two different strategies which start from fusing multiple features and then exploit the dictionary learning (DL) framework for better FR performance. The first strategy is a simple and efficient two-step model, which learns a fusion matrix from training face images to fuse multiple features and then learns class-specific dictionaries based on the fused features. The second one is a more effective model requiring more computational time that learns the fusion matrix and the class-specific dictionaries simultaneously within an iterative optimization procedure. Besides, the second model considers to separate the shared common components from class-specified dictionaries to enhance the discrimination power of the dictionaries. The proposed strategies, which integrate multi-feature fusion process and dictionary learning framework for FR, realize the following goals: (1) exploiting multiple features of face images for better FR performances;(2) learning a fusion matrix to merge the features into a more compact and more discriminative representation;(3) learning class-specific dictionaries with consideration of the c

关键词： Dictionary learning Face recognition Multiple features fusion Tensor decomposition sparse coding

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A Framework for Mining Signatures from Event Sequences and Its Applications in Healthcare Data

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2013年第2期35卷 272-285页

作者： Wang, Fei Lee, Noah Hu, Jianying Sun, Jimeng Ebadollahi, Shahram Laine, Andrew F. IBM TJ Watson Res Ctr Hawthorne NY 10532 USA 1010data New York NY 10169 USA Columbia Univ Dept Biomed Engn New York NY 10027 USA

This paper proposes a novel temporal knowledge representation and learning framework to perform large-scale temporal signature mining of longitudinal heterogeneous event data. The framework enables the representation, extraction, and mining of high-order latent event structure and relationships within single and multiple event sequences. The proposed knowledge representation maps the heterogeneous event sequences to a geometric image by encoding events as a structured spatial-temporal shape process. We present a doubly constrained convolutional sparse coding framework that learns interpretable and shift-invariant latent temporal event signatures. We show how to cope with the sparsity in the data as well as in the latent factor model by inducing a double sparsity constraint on the beta-divergence to learn an overcomplete sparse latent factor model. A novel stochastic optimization scheme performs large-scale incremental learning of group-specific temporal event signatures. We validate the framework on synthetic data and on an electronic health record dataset.

关键词： Temporal signature mining sparse coding dictionary learning nonnegative matrix factorization stochastic gradient descent beta-divergence

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Proto logic and neural subsymbolic reasoning

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JOURNAL OF LOGIC AND COMPUTATION 2013年第3期23卷 627-643页

作者： Wichert, Andreas Univ Tecn Lisboa Dept Informat INESC ID IST P-1100 Lisbon Portugal

The subsymbolic representation of the world often corresponds to a pattern that mirrors the world as described by the biological sense organs. sparse binary vectors can describe subsymbolic representations, which can be efficiently stored in associative memories. According to the production system theory, a geometrically based problem-solving model can be defined as a production system operating on subsymbols. Our goal is to form a sequence of associations, which lead to a desired state represented by subsymbols, from an initial state represented by subsymbols. A simple and universal heuristic function can be defined, which takes into account the relationship between the vector and the corresponding similarity of the represented object or state in the real world. The manipulation of the subsymbols is described by a simple proto logic, which verifies if a subset of subsymbols is present in a set of subsymbols.

关键词： associative memory problem-solving sparse coding symbols sub-symbols

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Learning class-specific dictionaries for digit recognition from spherical surface of a 3D ball

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MACHINE VISION AND APPLICATIONS 2013年第6期24卷 1213-1227页

作者： Wang, Donghui Kong, Shu Zhejiang Univ Dept Comp Sci & Technol Hangzhou 310027 Peoples R China

In the literature, very few researches have addressed the problem of recognizing the digits placed on spherical surfaces, even though digit recognition has already attracted extensive attentions and been attacked from various directions. As a particular example of recognizing this kind of digits, in this paper, we introduce a digit ball detection and recognition system to recognize the digit appearing on a 3D ball. The so-called digit ball is the ball carrying Arabic number on its spherical surface. Our system works under weakly controlled environment to detect and recognize the digit balls for practical application, which requires the system to keep on working without recognition errors in a real-time manner. Two main challenges confront our system, one is how to accurately detect the balls and the other is how to deal with the arbitrary rotation of the balls. For the first one, we develop a novel method to detect the balls appearing in a single image and demonstrate its effectiveness even when the balls are densely placed. To circumvent the other challenge, we use spin image and polar image for the representation of the balls to achieve rotation-invariance advantage. Finally, we adopt a dictionary learning-based method for the recognition task. To evaluate our system, a series of experiments are performed on real-world digit ball images, and the results validate the effectiveness of our system, which achieves 100 % accuracy in the experiments.

关键词： Digit ball recognition Dictionary learning sparse coding Circle detection Rotation invariance

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Combining affinity propagation with supervised dictionary learning for image classification

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NEURAL COMPUTING & APPLICATIONS 2013年第7-8期22卷 1301-1308页

作者： Xu, Bingxin Hu, Rukun Guo, Ping Beijing Normal Univ Lab Image Proc & Pattern Recognit Beijing 100875 Peoples R China

Recently support vector machines (SVM) using spatial pyramid matching (SPM) kernel have been highly successful in image classification applications. And linear spatial pyramid matching using sparse coding (ScSPM) scheme has been proposed to enhance the performance of SPM both in time and classification accuracy. In order to reduce the time complexity of dictionary construction process, sparse coding with affinity propagation method has been proposed in this paper. Because the dictionary used for sparse coding plays a key role in these methods, we also adopt supervised dictionary learning method to construct dictionary. The coding coefficients of each class have greater separability for SVM classification. Substantial experiments on Scene15 and CalTech101 image datasets have been conducted to investigate the performance of proposed approach in multi-class image classification;the results show that the approach can reach higher accuracy compared with ScSPM.

关键词： Image classification Supervised dictionary learning sparse coding Affinity propagation Spatial pyramid matching

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Compressive Framework for Demosaicing of Natural Images

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2013年第6期22卷 2356-2371页

作者： Moghadam, Abdolreza Abdolhosseini Aghagolzadeh, Mohammad Kumar, Mrityunjay Radha, Hayder Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA Eastman Kodak Co Rochester NY 14650 USA

Typical consumer digital cameras sense only one out of three color components per image pixel. The problem of demosaicing deals with interpolating those missing color components. In this paper, we present compressive demosaicing (CD), a framework for demosaicing natural images based on the theory of compressed sensing (CS). Given sensed samples of an image, CD employs a CS solver to find the sparse representation of that image under a fixed sparsifying dictionary Psi. As opposed to state of the art CS-based demosaicing approaches, we consider a clear distinction between the interchannel (color) and interpixel correlations of natural images. Utilizing some well-known facts about the human visual system, those two types of correlations are utilized in a nonseparable format to construct the sparsifying transform Psi. Our simulation results verify that CD performs better (both visually and in terms of PSNR) than leading demosaicing approaches when applied to the majority of standard test images.

关键词： Compressed sensing compressive demosaicing image demosaicing sparse coding

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Bayesian Learning of sparse Multiscale Image Representations

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2013年第12期22卷 4972-4983页

作者： Hughes, James Michael Rockmore, Daniel N. Wang, Yang LGS Innovat Appl Res & Technol ART Grp Florham Pk NJ 07932 USA Dartmouth Coll Dept Math & Comp Sci Hanover NH 03755 USA Santa Fe Inst Santa Fe NM 87501 USA Michigan State Univ Dept Math E Lansing MI 48824 USA

Multiscale representations of images have become a standard tool in image analysis. Such representations offer a number of advantages over fixed-scale methods, including the potential for improved performance in denoising, compression, and the ability to represent distinct but complementary information that exists at various scales. A variety of multiresolution transforms exist, including both orthogonal decompositions such as wavelets as well as nonorthogonal, overcomplete representations. Recently, techniques for finding adaptive, sparse representations have yielded state-of-the-art results when applied to traditional image processing problems. Attempts at developing multiscale versions of these so-called dictionary learning models have yielded modest but encouraging results. However, none of these techniques has sought to combine a rigorous statistical formulation of the multiscale dictionary learning problem and the ability to share atoms across scales. We present a model for multiscale dictionary learning that overcomes some of the drawbacks of previous approaches by first decomposing an input into a pyramid of distinct frequency bands using a recursive filtering scheme, after which we perform dictionary learning and sparse coding on the individual levels of the resulting pyramid. The associated image model allows us to use a single set of adapted dictionary atoms that is shared-and learned-across all scales in the model. The underlying statistical model of our proposed method is fully Bayesian and allows for efficient inference of parameters, including the level of additive noise for denoising applications. We apply the proposed model to several common image processing problems including non-Gaussian and nonstationary denoising of real-world color images.

关键词： Dictionary learning sparse coding multiscale image processing Bayesian statistical modeling

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Human Identification From ECG Signals Via sparse Representation of Local Segments

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IEEE SIGNAL PROCESSING LETTERS 2013年第10期20卷 937-940页

作者： Wang, Jin She, Mary Nahavandi, Saeid Kouzani, Abbas Deakin Univ Ctr Intelligent Syst Res Melbourne Vic Australia Deakin Univ Inst Frontier Mat Melbourne Vic Australia Deakin Univ Sch Engn Melbourne Vic Australia

This work proposes a novel framework to extract compact and discriminative features from Electrocardiogram (ECG) signals for human identification based on sparse representation of local segments. Specifically, local segments extracted from an ECG signal are projected to a small number of basic elements in a dictionary, which is learned from training data. A final representation is extracted by performing a max pooling procedure over all the sparse coefficient vectors in the ECG signal. Unlike most of existing methods for human identification from ECG signals which require segmentation of individual heartbeats or extraction of fiducial points, the proposed method does not need to segment individual heartbeats or detect any fiducial points. The method achieves an 99.48% accuracy on a 100 subjects dataset constructed from a publicly available database, which demonstrates that both local and global structural information are well captured to characterize the ECG signals.

关键词： sparse coding dictionary learning l(1) norm local features

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