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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Shi, Jun Jiang, Zhiguo Feng, Hao Ma, Yibing Beijing Univ Aeronaut & Astronaut Image Proc Ctr Sch Astronaut Beijing 100191 Peoples R China Beijing Key Lab Digital Media Beijing 100191 Peoples R China

ISBN: (纸本)9781479911141

Land cover segmentation can be viewed as topic assignment that the pixels are grouped into homogeneous regions according to different semantic topics in topic model. In this paper, we propose a novel topic model based on sparse coding for segmenting different kinds of land covers. Different from conventional topic models which generally assume each local feature descriptor is related to only one visual word of the codebook, our method utilizes sparse coding to characterize the potential correlation between the descriptor and multiple words. Therefore each descriptor can be represented by a small set of words. Furthermore, in this paper probabilistic Latent Semantic Analysis (pLSA) is applied to learn the latent relation among word, topic and document due to its simplicity and low computational cost. Experimental results on remote sensing image segmentation demonstrate the excellent superiority of our method over k-means clustering and conventional pLSA model.

关键词： remote sensing sparse coding pLSA land cover segmentation

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sparse coding-BASED CO-SALIENT OBJECT DETECTION WITH APPLICATION TO VIDEO ABSTRACTION 12

SPARSE CODING-BASED CO-SALIENT OBJECT DETECTION WITH APPLICA...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Chen, Duan-Yu Lin, Chuan-Yu Yang, Nien-Tzu Yu, Jen-Yu Yuan Ze Univ Dept Elect Engn Taoyuan Taiwan Ind Technol Res Inst Info & Comm Res Labs Hsinchu Taiwan

ISBN: (纸本)9781479902606

Automatic abstraction is important for video retrieval and browsing in a semantic manner. Detecting the focus of interest such as co-occurring objects in video frames automatically can benefit the tedious manual labelling process. However, detecting the co-occurring objects that is visually salient in video sequences is a challenging task. In this paper, in order to detect co-salient video objects efficiently, we first use the preattentive scheme to locate the co-salient regions in video frames and then measure the similarity between salient regions based on KL-divergence. In addition, to update preattentive patch set for co-salient objects, sparse coding is used for dictionary learning and further discrimination among co-salient objects. Finally a set of primary co-salient objects can be found across all video frames using our proposed filtering scheme. As a result, a video sequence can be automatically parsed based on the detection of co-occurring video objects. Our experiment results show that the proposed co-salient video objects modeling achieves high precision value about 85% and reveals its robustness and feasibility in videos.

关键词： Spatiotemporal analysis sparse coding Video abstraction

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sparse coding for Key Node Selection over Networks 17

Sparse Coding for Key Node Selection over Networks

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17th International Conference on Discovery Science (DS)

作者： Xu, Ye Rockmore, Dan Dartmouth Coll Dept Comp Sci Hanover NH 03755 USA Dartmouth Coll Dept Math Hanover NH 03755 USA Santa Fe Inst Santa Fe NM 87501 USA

ISBN: (纸本)9783319118123;9783319118116

The size of networks now needed to model real world phenomena poses significant computational challenges. Key node selection in networks, (KNSIN) presented in this paper, selects a representative set of nodes that preserves the sketch of original nodes in the network and thus, serves as a useful solution to this challenge. KNSIN is accomplished via a sparse coding algorithm that efficiently learns a basis set over the feature space defined by the nodes. By executing a stop criterion, KNSIN automatically learns the dimensionality of the node space and guarantees that the learned basis accurately preserves the sketch of the original node space. In experiments, we use two large scale network datasets to evaluate the proposed KNSIN framework. Our results on the two datasets demonstrate the effectiveness of the KNSIN algorithm.

关键词： Node Selection sparse coding Social Networks Reconstruction Cost

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Ensemble patch sparse coding: A feature learning method for classification of images with ambiguous edges

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 124卷 1-12页

作者： Lee, Jaehwan Kong, Taewoon Lee, Kichun Ind Engn Hanyang Univ Seoul Seoul South Korea Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA

sparse coding methods with a learned dictionary have been successful in several image classification problems. However, sparse representations from a unit dictionary may not contain full information when images are affected by environmental factors such as light, shadow, background, and so forth. In addition, sparse features formed by one dictionary can fall into a trap of singularity in training. To handle these problems, especially in images with ambiguous edges, we propose a new sparse coding method based on an ensemble of image patches. The proposed method includes an sparse-coding based image classification framework using image patches and their effective ensemble in an attempt to extract inherent structures from ambiguous-edge images. First, we transform such images into overlapped patches for better classification performance. Then, we assign patch-wise weights and seek to obtain optimal weights not by a single sparse representation but by ensemble learning. For obtaining optimal weights, we propose a two-step update scheme. We collectively update the weights of all patches in misclassified images first and then propagate the weights of misclassified patches to those of other overlapping patches in the images. Experimental results on the Northeastern University surface defect dataset and a close-up skin dataset show the proposed method achieved better classification accuracy than some existing methods and demonstrate the potential advantage of the proposed method in ambiguous-edge image classification. (C) 2019 Published by Elsevier Ltd.

关键词： sparse coding Ensemble Image classification Ambiguous edge

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Constructing Deep sparse coding Network for image classification

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PATTERN RECOGNITION 2017年 64卷 130-140页

作者： Zhang, Shizhou Wang, Jinjun Tao, Xiaoyu Gong, Yihong Zheng, Nanning Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Shaanxi Peoples R China

This paper introduces a deep model called Deep sparse-coding Network (DeepSCNet) to combine the advantages of Convolutional Neural Network (CNN) and sparse-coding techniques for image feature representation. DeepSCNet consists of four type of basic layers:" The sparse-coding layer performs generalized linear coding for local patch within the receptive field by replacing the convolution operation in CNN into sparse-coding. The Pooling layer and the Normalization layer perform identical operations as that in CNN. And finally the Map reduction layer reduces CPU/memory consumption by reducing the number of feature maps before stacking with the following layers. These four type of layers can be easily stacked to construct a deep model for image feature learning. The paper further discusses the multi-scale, multi-locality extension to the basic DeepSCNet, and the overall approach is fully unsupervised. Compared to CNN, training DeepSCNet is relatively easier even with training set of moderate size. Experiments show that DeepSCNet can automatically discover highly discriminative feature directly from raw image pixels.

关键词： sparse coding Deep Model Multi-scale Multi-locality Image classification

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Image classification by non-negative sparse coding, correlation constrained low-rank and sparse decomposition

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COMPUTER VISION AND IMAGE UNDERSTANDING 2014年 123卷 14-22页

作者： Zhang, Chunjie Liu, Jing Liang, Chao Xue, Zhe Pang, Junbiao Huang, Qingming Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Wuhan Univ Natl Engn Res Ctr Multimedia Software Sch Comp Wuhan 430072 Peoples R China Beijing Univ Technol Coll Comp Sci & Technol Beijing 100124 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intell Info Proc Beijing 100190 Peoples R China

We propose an image classification framework by leveraging the non-negative sparse coding, correlation constrained low rank and sparse matrix decomposition technique (CCLR-Sc+SPM). First, we propose a new non-negative sparse coding along with max pooling and spatial pyramid matching method (Sc+SPM) to extract local feature's information in order to represent images, where non-negative sparse coding is used to encode local features. Max pooling along with spatial pyramid matching (SPM) is then utilized to get the feature vectors to represent images. Second, we propose to leverage the correlation constrained low-rank and sparse matrix recovery technique to decompose the feature vectors of images into a low-rank matrix and a sparse error matrix by considering the correlations between images. To incorporate the common and specific attributes into the image representation, we still adopt the idea of sparse coding to recode the Sc+SPM representation of each image. In particular, we collect the columns of the both matrixes as the bases and use the coding parameters as the updated image representation by learning them through the locality-constrained linear coding (LLC). Finally, linear SVM classifier is trained for final classification. Experimental results show that the proposed method achieves or outperforms the state-of-the-art results on several benchmarks. (C) 2014 Elsevier Inc. All rights reserved.

关键词： sparse coding Image classification Low-rank decomposition Non-negative Correlation constrained

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Multi-frame image super resolution based on sparse coding

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NEURAL NETWORKS 2015年 66卷 64-78页

作者： Kato, Toshiyuki Hino, Hideitsu Murata, Noboru Waseda Univ Sch Sci & Engn Shinjuku Ku Tokyo 1698555 Japan Univ Tsukuba Dept Comp Sci Tsukuba Ibaraki 3058573 Japan

An image super-resolution method from multiple observation of low-resolution images is proposed. The method is based on sub-pixel accuracy block matching for estimating relative displacements of observed images, and sparse signal representation for estimating the corresponding high-resolution image, where correspondence between high- and low-resolution images are modeled by a certain degradation process. Relative displacements of small patches of observed low-resolution images are accurately estimated by a computationally efficient block matching method. The matching scores of the block matching are used to select a subset of low-resolution patches for reconstructing a high-resolution patch, that is, an adaptive selection of informative low-resolution images is realized. The proposed method is shown to perform comparable or superior to conventional super-resolution methods through experiments using various images. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Image super resolution Multi-frame super-resolution sparse coding

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Provably Accurate Double-sparse coding

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JOURNAL OF MACHINE LEARNING RESEARCH 2019年第1期20卷 1-43页

作者： Nguyen, Thanh V. Wong, Raymond K. W. Hegde, Chinmay Iowa State Univ Dept Elect & Comp Engn Ames IA 50011 USA Texas A&M Univ Dept Stat College Stn TX 77843 USA

sparse coding is a crucial subroutine in algorithms for various signal processing, deep learning, and other machine learning applications. The central goal is to learn an overcomplete dictionary that can sparsely represent a given input dataset. However, a key challenge is that storage, transmission, and processing of the learned dictionary can be untenably high if the data dimension is high. In this paper, we consider the double-sparsity model introduced by Rubinstein et al. (2010b) where the dictionary itself is the product of a fixed, known basis and a data-adaptive sparse component. First, we introduce a simple algorithm for double-sparse coding that can be amenable to efficient implementation via neural architectures. Second, we theoretically analyze its performance and demonstrate asymptotic sample complexity and running time benefits over existing (provable) approaches for sparse coding. To our knowledge, our work introduces the first computationally efficient algorithm for double-sparse coding that enjoys rigorous statistical guarantees. Finally, we corroborate our theory with several numerical experiments on simulated data, suggesting that our method may be useful for problem sizes encountered in practice.

关键词： sparse coding provable algorithms unsupervised learning

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Denoising natural images based on a modified sparse coding algorithm

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APPLIED MATHEMATICS AND COMPUTATION 2008年第2期205卷 883-889页

作者： Shang, Li Suzhou Vocat Univ Dept Elect Informat Engn Jiangsu 215104 Peoples R China

This paper proposes a novel image reconstruction method for natural images using a modified sparse coding (SC) algorithm proposed by us. This SC algorithm exploited the maximum Kurtosis as the maximizing sparse measure criterion at one time, a fixed variance term of sparse coefficients is used to yield a fixed information capacity. On the other hand, in order to improve the convergence speed, we use a determinative basis function, which is obtained by a fast fixed-point independent component analysis (FastICA) algorithm, as the initialization feature basis function of our sparse coding algorithm instead of using a random initialization matrix. The experimental results show that by using our SC algorithm, the feature basis vectors of natural images can be successfully extracted. Then, exploiting these features, the original images can be reconstructed easily. Furthermore, compared with the standard ICA method, the experimental results show that our SC algorithm is indeed efficient and effective in performing image reconstruction task. (C) 2008 Elsevier Inc. All rights reserved.

关键词： sparse coding Kurtosis Fixed variance Image feature extraction Image reconstruction

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An MDL Framework for sparse coding and Dictionary Learning

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2012年第6期60卷 2913-2927页

作者： Ramirez, Ignacio Sapiro, Guillermo Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

The power of sparse signal modeling with learned overcomplete dictionaries has been demonstrated in a variety of applications and fields, from signal processing to statistical inference and machine learning. However, the statistical properties of these models, such as underfitting or overfitting given sets of data, are still not well characterized in the literature. As a result, the success of sparse modeling depends on hand-tuning critical parameters for each data and application. This work aims at addressing this by providing a practical and objective characterization of sparse models by means of the minimum description length (MDL) principle-a well-established information-theoretic approach to model selection in statistical inference. The resulting framework derives a family of efficient sparse coding and dictionary learning algorithms which, by virtue of the MDL principle, are completely parameter free. Furthermore, such framework allows to incorporate additional prior information to existing models, such as Markovian dependencies, or to define completely new problem formulations, including in the matrix analysis area, in a natural way. These virtues will be demonstrated with parameter-free algorithms for the classic image denoising and classification problems, and for low-rank matrix recovery in video applications. However, the framework is not limited to this imaging data, and can be applied to a wide range of signal and data types and tasks.

关键词： Classification denoising dictionary learning minimum description length principle (MDL) low-rank matrix completion sparse coding

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