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Graph regularized sparse coding for 3D shape clustering

KNOWLEDGE-BASED SYSTEMS 2016年第Jan.15期92卷 92-103页

作者： Ben Hamza, A. Concordia Univ Concordia Inst Informat Syst Engn Montreal PQ Canada

Feature descriptors have become an increasingly important tool in shape analysis. Features can be extracted and subsequently used to design robust signatures for shape retrieval, correspondence, classification and clustering. In this paper, we present a graph-theoretic framework for 3D shape clustering using the biharmonic distance map and graph regularized sparse coding. While this work focuses primarily on clustering, our approach is fairly general and can be used to tackle other 3D shape analysis problems. In order to seamlessly capture the similarity between feature descriptors, we perform shape clustering on mid-level features that are generated via graph regularized sparse coding. Extensive experiments are carried out on three standard 3D shape benchmarks to demonstrate the much better performance of the proposed clustering approach in comparison with recent state-of-the-art methods. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Clustering Laplace-Beltrami Biharmonic distance sparse coding

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Fast convolutional sparse coding with separable filters

Fast convolutional sparse coding with separable filters

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Gustavo Silva Jorge Quesada Paul Rodriguez Brendt Wohlberg Department of Electrical Engineering Pontificia Universidad Catolica del Peru Lima Peru T-5 Applied Mathematics and Plasma Physics Los Alamos National Laboratory NM 87545 USA

ISBN: (纸本)9781509041183

Convolutional sparse representations (CSR) of images are receiving increasing attention as an alternative to the usual independent patch-wise application of standard sparse representations. For CSR the dictionary is a filter bank of non-separable 2D filters, and the representation itself can be viewed as the synthesis dual of the analysis representation provided by a single level of a convolutional neural network (CNN). The current state-of-the-art convolutional sparse coding (CSC) algorithms achieve their computational efficiency by applying the convolutions in the frequency domain. It has been shown that any given 2D non-separable filter bank can be approximated as a linear combination of a relatively small number of separable filters. This approximation has been exploited for computationally efficient CNN implementations, but has thus far not been considered for convolutional sparse coding. In this paper we propose a computationally efficient algorithm, that apply the convolution in the spatial domain, to solve the CSC problem when the corresponding dictionary filters are separable. Our algorithm, based on the ISTA framework, use a two-term penalty function to attain competitive results when compared to the state-of-the-art methods in terms of computational performance, sparsity and reconstruction quality.

关键词： Convolutional sparse Representation Convolutional sparse coding Separable Filters sparse coding Filter banks Separable Filters Frequency-domain analysis efficient algorithm spatial domain Dictionaries as Topic Cheyne-Stokes Respiration Computational efficiency Linear Combination Convolution representations two-dimensional filter Banks

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Image denoising via sparse coding using eigenvectors of graph Laplacian

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DIGITAL SIGNAL PROCESSING 2016年 50卷 114-122页

作者： Tang, Yibin Chen, Ying Xu, Ning Jiang, Aimin Zhou, Lin Hohai Univ Coll Internet Things Engn Changzhou Peoples R China Southeast Univ Sch Informat Sci & Engn Nanjing Jiangsu Peoples R China

Image denoising plays an important role in image processing, which aims to separate clean images from the noisy images. A number of methods have been presented to deal with this practical problem in the past decades. In this paper, a sparse coding algorithm using eigenvectors of the graph Laplacian (EGL-SC) is proposed for image denoising by considering the global structures of images. To exploit the geometry attributes of images, the eigenvectors of the graph Laplacian, which are derived from the graph of noised patches, are incorporated in the sparse model as a set of basis functions. Sequently, the corresponding sparse coding problem is presented and efficiently solved with a relaxed iterative method in the framework of the double sparsity model. Meanwhile, as the denoising performance of the EGL-SC significantly depends on the number of the used eigenvectors, an optimal strategy for the number selection is employed. A parameter called as out-of-control rate is set to record the percentage of the denoised patches that suffer from serious residual errors in the sparse coding procedure. Thus, with the eigenvector number increasing, the appropriate number can be heuristically selected when the out of-control rate falls below an empirical threshold. Experiments illustrate that the EGL-SC can achieve a better performance than some other well-developed denoising methods, especially in the structural similarity index for the noise of large deviations. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Eigenvector Graph Laplacian Image denoising sparse coding

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Collaborative sparse coding for Multiview Action Recognition

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IEEE MULTIMEDIA 2016年第4期23卷 80-87页

作者： Wang, Wei Yan, Yan Zhang, Luming Hong, Richang Sebe, Nicu Univ Trento Multimedia & Human Understanding Grp Trento Italy Univ Trento Multimedia & Human Understanding Grp MHUG Grp Trento Italy Hefei Univ Technol Dept Comp & Informat Hefei Peoples R China Univ Trento Dept Comp Sci Trento Italy

Self-similarity matrices (SSMs) show outstanding performance in extracting view-invariant features in multiview action recognition except when there's a large view change. The proposed approach increases SSM robus... 详细信息

关键词： Encoding Videos Mathematical Model Collaboration Dictionaries Feature Extraction Robustness Software Engineering Collaborative Filtering sparse coding Dictionary Multiview Action Recognition Multimedia Quality Modeling Data Analysis Internet Web Technologies

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Binary sparse coding of Convolutive Mixtures for Sound Localization and Separation via Spatialization

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2016年第3期64卷 567-579页

作者： Asaei, Afsaneh Taghizadeh, Mohammad Javad Haghighatshoar, Saeid Raj, Bhiksha Bourlard, Herve Cevher, Volkan Idiap Res Inst CH-1920 Martigny Switzerland Huawei European Res Ctr D-80992 Munich Germany Tech Univ Berlin D-10623 Berlin Germany Carnegie Mellon Univ Language Technol Inst Pittsburgh PA 15213 USA Ecole Polytech Fed Lausanne CH-1015 Lausanne Switzerland

We propose a sparse coding approach to address the problem of source-sensor localization and speech reconstruction. This approach relies on designing a dictionary of spatialized signals by projecting the microphone array recordings into the array manifolds characterized for different locations in a reverberant enclosure using the image model. sparse representation over this dictionary enables identifying the subspace of the actual recordings and its correspondence to the source and sensor locations. The speech signal is reconstructed by inverse filtering the acoustic channels associated to the array manifolds. We provide rigorous analysis on the optimality of speech reconstruction by elucidating the links between inverse filtering and source separation followed by deconvolution. This procedure is evaluated for localization, reconstruction and recognition of simultaneous speech sources using real data recordings. The results demonstrate the effectiveness of the proposed approach and compare favorably against beamforming and independent component analysis techniques.

关键词： Microphone array multiparty (overlapping) speech recognition reverberation sound spatialization source localization and separation sparse coding

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Image classification using label constrained sparse coding

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MULTIMEDIA TOOLS AND APPLICATIONS 2016年第23期75卷 15619-15633页

作者： Liu, Ruijun Chen, Yi Zhu, Xiaobin Hou, Kun Beijing Technol & Business Univ Beijing Peoples R China

sparse coding has been widely used for feature encoding in recent years. However, the encoded parameters' similarity is ignored with sparse coding. Besides, the label information from which class the local feature is extracted is also ignored. To solve this problem, in this paper, we propose a novel feature encoding method called label constrained sparse coding (LCSC) for visual representation. The visual similarities between local features are jointly considered with the corresponding label information of local features. This is achieved by combining the label constraints with the encoding of local features. In this way, we can ensure that similar local features with the same label are encoded with similar parameters. Local features with different labels are encoded with dissimilar parameters to increase the discriminative power of encoded parameters. Besides, instead of optimizing for the coding parameter of each local feature separately, we jointly encode the local features within one sub-region in the spatial pyramid way to combine the spatial and contextual information of local features. We apply this label constrained sparse coding technique for classification tasks on several public image datasets to evaluate its effectiveness. The experimental results shows the effectiveness of the proposed method.

关键词： Label consistent sparse coding Image classification Local feature encoding

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The Infrared Small Targets Detection based on Improved sparse coding 2017

The Infrared Small Targets Detection based on Improved Spars...

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Proceedings of the 2017 2nd International Conference on Communication and Information Systems

作者： Yan Liu Yujia Liu Haiying Zhang Software School of Xiamen University Fujian Xiamen School of Mathematical Sciences Xiamen University Fujian Xiamen

ISBN: (纸本)9781450353489

Due to the lack of obvious discriminative properties, infrared small target detection is a hard work in automatic target recognition for a long time. Recently, sparse coding is introduced into the field to estimate the background, and achieve good results. However, the problem of dictionary updating remains unresolved, which directly affects the accuracy of background estimation and the target detection results. Aiming at this problem, in this paper, an improved sparse coding method is proposed. It exploits the inter-frame correlation characteristics the dynamic background sequence to build the initial dictionary, and then he updating is completed iteratively by using a weighted value of the dictionary built last time. Additionally, to guarantee the convergence of the least-square method, we incorporate SVD to get an optimal error upper limit. The experiments demonstrate that the method can significantly improve the accuracy of background estimation and, further, compared with the original sparse representation method it has a superior clutter suppression ratio.

关键词： Small target Dictionary Updating SVD sparse coding

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Information integration for ground-based cloud classification using joint consistent sparse coding in heterogeneous sensor network

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SIGNAL PROCESSING 2016年 126卷 165-172页

作者： Liu, Shuang Zhang, Zhong Cao, Xiaozhong Tianjin Normal Univ Coll Elect & Commun Engn Tianjin Peoples R China China Meteorol Adm Meteorol Observat Ctr Beijing Peoples R China

Although sparsity-based algorithm has emerged as an extremely powerful tool for information integration, it neglects the relationship of heterogeneous features and coding coefficients from the same class in the training stage, which may cause declining of the classification performance. In this paper, we focus on information integration for ground based cloud classification in heterogeneous sensor network (HSN), and propose a novel coding strategy named joint consistent sparse coding (JCSC) to overcome the drawbacks of traditional sparse coding. In order to integrate information effectively, we add a joint sparse regularization to learn the relationship of heterogeneous features. Moreover, we utilize the consistent constraint on coding coefficients. In this way, coding coefficients from the same class can be forced to their mean vector, and therefore they are more compact and discriminative. The experimental results demonstrate that our method achieves better performance than the state-of-the-art methods. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Information integration Heterogeneous sensor network Ground-based cloud classification sparse coding

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Exploring Dictionary Diversity for Improved sparse coding Based Speaker Verification

Exploring Dictionary Diversity for Improved Sparse Coding Ba...

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IEEE India Council International Conference

作者： Nagendra Kumar Ganji Sreeram Rohit Sinha Department of Electronics and Electrical Engineering Indian Institute of Technology Guwahati

ISBN: (纸本)9781538643198

Recently we have proposed the sparse representation over exemplar and learned dictionaries for speaker verification (SV). When compared with the i-vector Gaussian probabilistic linear discriminant analysis (PLDA) system, such SV systems are noted to yield good detection cost factor, but with significant degraded equal error rate (EER). The reason for the degraded EER is that some of the true trials could not select the dictionary atom corresponding to the true speaker during sparse coding. Towards addressing this problem, in this work, we explore the diversity in sparse coding through the use of multiple exemplar dictionaries. These procedures of learning dictionaries are created by partitioning the development data by learning multi-column dictionary per speaker using KSVD algorithm which are then combined to produce a learned exemplar dictionary. On fusion of the scores obtained from these multiple exemplar dictionaries, a significant reduction in the EER is obtained, but with the lower detection cost factor in contrast to the simple exemplar dictionary. Further, when the scores of the simple exemplar dictionary are also included in fusion, improvement in both the performance measures are obtained. The final system performances are quite comparable to the state-of-the-art i-vector Gaussian PLDA system with much lower computational cost.

关键词： Dictionaries as Topic speaker recognition sparse coding test sample energy efficiency ratio Loudspeakers System performance sparse representation

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Locally linear embedded sparse coding for image representation

Locally linear embedded sparse coding for image representati...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Lingdao Sha Dan Schonfeld Jing Wang University of Illinois at Chicago Electrical and Computer Engineering 851 S Morgan St United States of America University of Illinois at Chicago Mathematics Statistics and Computer Science 851 S Morgan St United States of America

ISBN: (纸本)9781509041183

Recently, sparse coding has been widely and successfully used in image classification, noise reduction, texture synthesis and audio processing. Although traditional sparse coding method with fixed dictionaries like wavelet and curvelet can produce promising results, unsupervised sparse coding has shown its advantage by optimizing the dictionary adaptively. However, existing unsupervised sparse coding failed to consider the high dimensional manifold information within data. Recently, a graph regularized sparse coding method has shown outstanding performance by incorporating graph laplacian manifold information. In this paper, we proposed a sparse coding method called locally linear embedded sparse coding, to consider the local manifold structure as well as learning the sparse representation. We also provided a novel modified online dictionary learning method which iteratively utilizes modified least angle regression and block coordinate descent method to solve the problem. Instead of getting entire coefficient matrix then update dictionary matrix, our method updates coefficient vector and dictionary matrix in each inner iteration. Extensive experimental results have demonstrated the efficiency and accuracy of our method in image clustering.

关键词： Locally linear embedding sparse coding manifold learning online dictionary learning least angle regression image clustering SIFT sparse coding image representation Dictionaries as Topic Sifting

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