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20th IEEE International Conference on Image Processing (ICIP)

作者： Tang, Xun Zhang, Shengping Yao, Hongxun Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China Brown Univ Dept Cognit Linguist & Psychol Sci Providence RI 02912 USA

ISBN: (纸本)9781479923410

In this paper, we present a novel method based on sparsely coded motion attention for detecting abnormal events in crowded scenes. Unlike existing sparse coding based approaches, our model does not need to learn a dictionary and directly sparsely codes the motion features of the center patches with features of its surrounding patches. The sparse coding error is used to measure the motion attention intensity of the center patch. To reflect the crowd abnormal intensity, an online updated weighting scheme is designed to obtain the global activity intensity map. Two publicly available datasets-UMN dataset and UCSD Ped1 dataset are utilized to evaluate our approach in detecting global abnormal event and local abnormal event, respectively. The experiments show our method achieves the promising performance and is competitive with the state-of-the-art approaches.

关键词： sparse coding activity intensity crowd behavior abnormal detection

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sparse coding Predicts Optic Flow Specifities of Zebrafish Pretectal Neurons 27th

Sparse Coding Predicts Optic Flow Specifities of Zebrafish P...

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27th International Conference on Artificial Neural Networks (ICANN)

作者： Ecke, Gerrit A. Mikulasch, Fabian A. Bruijns, Sebastian A. Witschel, Thede Arrenberg, Aristides B. Mallot, Hanspeter A. Univ Tubingen Dept Biol Tubingen Germany

ISBN: (纸本)9783030014247

Zebrafish pretectal neurons exhibit specificities for large-field optic flow patterns associated with rotatory or translatory body motion. We investigate the hypothesis that these specificities reflect the input statistics of natural optic flow. Realistic motion sequences were generated using computer graphics simulating self-motion in an underwater scene. Local retinal motion was estimated with a motion detector and encoded in four populations of directionally tuned retinal ganglion cells, represented as two signed input variables. This activity was then used as input into one of two learning networks: a sparse coding network (competitive learning) and backpropagation network (supervised learning). Both simulations develop specificities for optic flow which are comparable to those found in a neurophysiological study [8], and relative frequencies of the various neuronal responses are best modeled by the sparse coding approach. We conclude that the optic flow neurons in the zebrafish pretectum do reflect the optic flow statistics. The predicted vectorial receptive fields show typical optic flow fields but also "Gabor" and dipole-shaped patterns that likely reflect difference fields needed for reconstruction by linear superposition.

关键词： Optic flow sparse coding Optimality Pretectum Egomotion detection

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sparse coding of Symbolically Represented Motion Patterns and Top-Down Activation based on an Associative Memory Model

Sparse Coding of Symbolically Represented Motion Patterns an...

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SICE Annual Conference

作者： Kadone, Hideki Nakamura, Yoshihiko Univ Tsukuba Ctr Cybern Res Tsukuba Ibaraki 305 Japan Univ Tokyo Dept Mechano Informat Tokyo 1138654 Japan

ISBN: (纸本)9784907764432

As has been claimed by Barlow[2], and reported by some recent neuro-physiological researches, at higher levels in the hierarchy of representations in the brain, sparse coding is adopted. sparse coding is a kind of neural representation in which a very small number of neurons fire selectively. Because of the small overlaps, the codes have the property of uniform metric, which is very different from the physically sensed continuous patterns. It is also known to be efficient in memory capacity, energy consumption and combinatorial computation. Then the problem is, how sparse codes representing concepts can be generated from accumulated episodic memories that are inevitably complex distributed sensory-motor patterns. We propose here a system that generates sparse codes of concepts of motions from accumulated feature vectors of observed motion patterns, by extending our previous research[9]. We apply to this problem an associative memory dynamics model with a self-organizing nonmonotonic activation function, which automatically finds out the hierarchical cluster structures in the stored data. Based on our analysis of the dynamics of this model, we design an output function for the attractors, which can generate the sparse codes of the symbols of motion patterns.

关键词： symbol acquisition sparse coding motion pattern neural network nonlinear dynamics

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sparse coding Image Denoising Based on Saliency Map Weight

Sparse Coding Image Denoising Based on Saliency Map Weight

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18th International Conference on Neural Information Processing (ICONIP)

作者： Zhao, Haohua Zhang, Liqing Shanghai Jiao Tong Univ MOE Microsoft Key Lab Intelligent Comp & Intellig Dept Comp Sci & Engn Shanghai 200030 Peoples R China

ISBN: (纸本)9783642249570;9783642249587

Saliency maps provide a measurement of people's attention to images. People pay more attention to salient;regions and perceive more information in them. Image denoising enhances image quality by reducing the noise in contaminated images. Here we implement an algorithm framework to use a saliency map as weight to manage tradeoffs in denoising using sparse coding. Computer simulations confirm that the proposed method achieves better performance than a method without the saliency map.

关键词： sparse coding saliency map image denoise

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sparse coding WITH ANOMALY DETECTION

SPARSE CODING WITH ANOMALY DETECTION

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23rd IEEE International Workshop on Machine Learning for Signal Processing (MLSP)

作者： Adler, Amir Elad, Michael Hel-Or, Yacov Rivlin, Ehud Technion Israel Inst Technol Dept Comp Sci IL-32000 Haifa Israel Interdiscplinary Ctr Dept Comp Sci Herzliyya Israel

ISBN: (纸本)9781479911806

We consider the problem of simultaneous sparse coding and anomaly detection in a collection of data vectors. The majority of the data vectors are assumed to conform with a sparse representation model, whereas the anomaly is caused by an unknown subset of the data vectors-the outliers which significantly deviate from this model. The proposed approach utilizes the Alternating Direction Method of Multipliers (ADMM) to recover simultaneously the sparse representations and the outliers components for the entire collection. This approach provides a unified solution both for jointly sparse and independently sparse data vectors. We demonstrate the usefulness of the proposed approach for irregular heartbeats detection in Electrocardiogram (ECG) and specular reflectance removal from natural images.

关键词： sparse coding anomaly detection ADMM arrythmia detection specular reflectance removal

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sparse coding of Total Variability Matrix 16

Sparse Coding of Total Variability Matrix

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16th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2015)

作者： Xu, Longting Lee, Kong Aik Li, Haizhou Yang, Zhen Nanjing Univ Posts & Telecommun Broadband Wireless Commun & Sensor Network Techno Nanjing Jiangsu Peoples R China ASTAR Inst Infocomm Res Singapore Singapore

ISBN: (纸本)9781510817906

In text-independent speaker verification, it has been shown effective to represent the variable-length and information rich speech utterances using fixed-dimensional vectors, for instance, in the form of i-vectors. An i-vector is a low-dimensional vector in the so-called total variability space represented with a thin and tall rectangular matrix. Taking each row of the total variability matrix as a random vector, we look into the redundancy in representing the total variability space. We show that the total variability matrix is compressible and such characteristic could be exploited to reduce the memory and computational requirement in i-vector extraction. We also show that the existing sparse coding and dictionary learning techniques could be easily adapted for this purpose. Experiments on NIST SRE'10 dataset confirm that the total variability matrix could be represented with a smaller matrix without affecting the performance.

关键词： speaker verification i-vector sparse coding

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Multi-sensor image super-resolution with fuzzy cluster by using multi-scale and multi-view sparse coding for infrared image

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第23期76卷 24871-24902页

作者： Yang, Xiaomin Wu, Wei Liu, Kai Chen, Weilong Zhang, Ping Zhou, Zhili Sichuan Univ Coll Elect & Informat Engn Chengdu 610064 Sichuan Peoples R China Sichuan Univ Sch Elect Engn & Informat Chengdu 610064 Sichuan Peoples R China Sichuan Normal Univ Coll Movie & Media Chengdu 610018 Sichuan Peoples R China Univ Elect Sci & Technol China Graph Image & Signal Proc Applicat Lab Chengdu 611731 Sichuan Peoples R China Nanjing Univ Informat Sci & Technol Jiangsu Engn Ctr Network Monitoring Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Jiangsu Peoples R China

Super-resolution (SR) methods are effective for generating a high-resolution image from a single low-resolution image. However, four problems are observed in existing SR methods. (1) They cannot reconstruct many details from a low-resolution infrared image because infrared images always lack detailed information. (2) They cannot extract the desired information from images because they do not consider that images naturally come at different scales in many cases. (3) They fail to reveal different physical structures of low-resolution patch because they extract features from a single view. (4) They fail to extract all the different patterns because they use only one dictionary to represent all patterns. To overcome these problems, we propose a novel SR method for infrared images. First, we combine the information of high-resolution visible light images and low-resolution infrared images to improve the resolution of infrared images. Second, we use multiscale patches instead of fixed-size patches to represent infrared images more accurately. Third, we use different feature vectors rather than a single feature to represent infrared images. Finally, we divide training patches into several clusters, and multiple dictionaries are learned for each cluster to provide each patch with a more accurate dictionary. In the proposed method, clustering information for low-resolution patches is learnt by using fuzzy clustering theory. Experiments validate that the proposed method yields better results in terms of quantization and visual perception than the state-of-the-art algorithms.

关键词： Multi-sensor Super-resolution sparse coding Infrared image Dictionary learning Multiview representation Fuzzy clustering theory

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Smooth sparse coding via marginal regression for learning sparse representations

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ARTIFICIAL INTELLIGENCE 2016年 238卷 83-95页

作者： Balasubramanian, Krishnakumar Yu, Kai Lebanon, Guy Univ Wisconsin Dept Stat Madison WI 53706 USA Horizong Robot Beijing Peoples R China LinkedIn Mountain View CA USA

We propose and analyze a novel framework for learning sparse representations based on two statistical techniques: kernel smoothing and marginal regression. The proposed approach provides a flexible framework for incorporating feature similarity or temporal information present in data sets via non-parametric kernel smoothing. We provide generalization bounds for dictionary learning using smooth sparse coding and show how the sample complexity depends on the L-1 norm of kernel function used. Furthermore, we propose using marginal regression for obtaining sparse codes which significantly improves the speed and allows one to scale to large dictionary sizes easily. We demonstrate the advantages of the proposed approach, both in terms of accuracy and speed by extensive experimentation on several real data sets. In addition, we demonstrate how the proposed approach can be used for improving semi-supervised sparse coding. (C) 2016 Elsevier B.V. All rights reserved.

关键词： sparse coding Dictionary learning Vision

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A highly secure oblivious sparse coding-based watermarking system for ownership verification

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第4期42卷 2224-2233页

作者： Tareef, Afaf Al-Ani, Ahmed Univ Sydney Sch Informat Technol Sydney NSW 2006 Australia Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW 2007 Australia

In the last few decades, the watermarking security issue has become one of the main challenges facing the design of watermarking techniques. In this paper, a secure oblivious watermarking system, based on sparse coding (SC) is proposed in order to tackle the three most critical watermarking security problems, i.e., unauthorized reading, false positive detection, and multiple claims of ownership problems, as well as optimize the fidelity, imperceptibility, and robustness characteristics. The reason for incorporating SC in the proposed system is to encode the watermark image before embedding it in the host image. This process is implemented using the well-known Stagewise Orthogonal Matching Pursuit (StOMP) method and an orthogonal dictionary that is derived from the host image itself. The watermark embedding is implemented in the transform domain of the Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) of the host image. The proposed system is oblivious, as it does not need the original host image when extracting the embedded watermark. In addition, it is suitable for both bi-level and gray-level watermarks, and can accommodate large watermarks that are up to half the size of the host image. The proposed SC-DWT-SVD based watermarking scheme is tested for various malicious and un-malicious attacks and the experimental results show that it realizes the security requirement as it tackles the false positive detection and multiple claims of ownership problems on one hand and generates an encryption form of the watermark on the other hand. In addition, the added security does not compromise the imperceptibility and robustness aspects of the proposed technique and hence can be considered to be comparable or superior to other up-to-date watermarking techniques. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： False positive detection problem Multiple claims of ownership sparse coding Watermarking security

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LSALSA: accelerated source separation via learned sparse coding

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MACHINE LEARNING 2019年第8-9期108卷 1307-1327页

作者： Cowen, Benjamin Saridena, Apoorva Nandini Choromanska, Anna NYU Tandon Sch Engn 6 MetroTech Ctr Brooklyn NY 11201 USA

We propose an efficient algorithm for the generalized sparse coding (SC) inference problem. The proposed framework applies to both the single dictionary setting, where each data point is represented as a sparse combination of the columns of one dictionary matrix, as well as the multiple dictionary setting as given in morphological component analysis (MCA), where the goal is to separate a signal into additive parts such that each part has distinct sparse representation within an appropriately chosen corresponding dictionary. Both the SC task and its generalization via MCA have been cast as l(1)-regularized optimization problems of minimizing quadratic reconstruction error. In an effort to accelerate traditional acquisition of sparse codes, we propose a deep learning architecture that constitutes a trainable time-unfolded version of the split augmented lagrangian shrinkage algorithm (SALSA), a special case of the alternating direction method of multipliers (ADMM). We empirically validate both variants of the algorithm, that we refer to as learned-SALSA (LSALSA), on image vision tasks and demonstrate that at inference our networks achieve vast improvements in terms of the running time and the quality of estimated sparse codes on both classic SC and MCA problems over more common baselines. We also demonstrate the visual advantage of our technique on the task of source separation. Finally, we present a theoretical framework for analyzing LSALSA network: we show that the proposed approach exactly implements a truncated ADMM applied to a new, learned cost function with curvature modified by one of the learned parameterized matrices. We extend a very recent stochastic alternating optimization analysis framework to show that a gradient descent step along this learned loss landscape is equivalent to a modified gradient descent step along the original loss landscape. In this framework, the acceleration achieved by LSALSA could potentially be explained by the network's ability

关键词： sparse coding Morphological component analysis Deep learning

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