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Color video denoising using epitome and sparse coding

EXPERT SYSTEMS WITH APPLICATIONS 2015年第2期42卷 751-759页

作者： Lee, Hwea Yee Hoo, Wai Lam Chan, Chee Seng Univ Malaya Ctr Image & Signal Proc Fac Comp Sci & Informat Technol Kuala Lumpur 50603 Malaysia

Denoising is a process that remove noise from a signal. In this paper, we present a unified framework to deal with video denoising problems by adopting a two-steps process, namely the video epitome and sparse coding. First, the video epitome will summarize the video contents and remove the redundancy information to generate a single compact representation to describe the video content. Second, employing the single compact representation as an input, the sparse coding will generate a visual dictionary for the video sequence by estimating the most representative basis elements. The fusion of these two methods have resulted an enhanced, compact representation for the denoising task. Experiments on the publicly available datasets have shown the effectiveness of our proposed system in comparison to the state-of-the-art algorithms in the video denoising task. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Video denoising Video epitome sparse coding

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How to tune sparse network coding over wireless links

How to tune sparse network coding over wireless links

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IEEE Wireless Communications and Networking Conference (WCNC)

作者： Garrido, Pablo Lucani, Daniel E. Aguero, Ramon Univ Cantabria Santander Spain Aalborg Univ Dept Elect Syst Aalborg Denmark

ISBN: (纸本)9781509041831

Despite their high computational complexity, Random Linear Network coding (RLNC) techniques have been shown to offer a good robustness against packet erasure wireless channels. Some approaches have been recently proposed to reduce such computational burden, for both encoder and decoder elements. One of those approaches are the so-called Tunable sparse Network coding (TSNC) techniques, which advocate limiting the number of packets that are combined to build a coded packet. They also propose dynamically adapting the corresponding sparsity level, as the transmission evolves, although an optimum tuning policy has not been yet found. In this paper we present a TSNC implementation that exploits a novel analytical model to estimate the probability of generating an innovative packet (linearly independent combination), given the current status at the decoder. Taking advantage of the model's accuracy, the proposed scheme offers a better trade-off between computational complexity and network performance. Furthermore, we broaden the analysis of TSNC techniques by thoroughly assessing their behavior over wireless networks using the ns-3 platform. The results yield a remarkable complexity reduction (approximate to 3.33x less complexity), without jeopardizing network performance.

关键词： Random Linear coding sparse coding Network coding

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Unsupervised feature learning based on sparse coding and spectral clustering for segmentation of synthetic aperture radar images

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IET COMPUTER VISION 2015年第5期9卷 629-638页

作者： Rahmani, Masoumeh Akbarizadeh, Gholamreza Shahid Chamran Univ Ahvaz SCU Dept Elect Engn Fac Engn Ahvaz *** Iran

Synthetic aperture radar (SAR) image segmentation is fundamental for the interpretation and understanding of these images. In this process, the representation of SAR image features plays an important role. Spectral clustering is an image segmentation method making it possible to combine features and cues. This study presents a new spectral clustering method using unsupervised feature learning (UFL). In this method, the SAR image is primarily processed by the non-negative matrix factorisation (NMF) algorithm and then non-negative features containing spatial structure information are extracted. Afterwards, the extracted features are learned using a sparse coding algorithm to increase the discrimination power of the features. sparse coding is an unsupervised learning algorithm which finds the patterns or high-level semantics of the data. Ultimately, the SAR image segmentation operation is performed by applying spectral clustering on learned features. In this method, sparse coding learns features and simultaneously creates the similarity function required in spectral clustering through the production of sparse coefficients. Therefore this method avoids the Gaussian similarity function, which has a problem with scale parameter adjustment that is one of the drawbacks of spectral clustering methods. The results demonstrate that, compared with wavelet and GLCM features, NMF features manage to obtain more meaningful information and provide a better SAR image segmentation result. The results have also demonstrated that SAR image segmentation using learned features is significantly improved compared with segmentation by unlearned features. The experimental results indicate the effect of UFL on SAR image segmentation.

关键词： synthetic aperture radar radar imaging image coding image segmentation feature extraction learning (artificial intelligence) matrix decomposition radar computing unsupervised feature learning sparse coding synthetic aperture radar images segmentation SAR image feature representation image segmentation method spectral clustering method nonnegative matrix factorisation algorithm spatial structure information feature extraction sparse coding algorithm SAR image segmentation operation spectral clustering Gaussian similarity function SAR image segmentation

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A Pruning Algorithm for Extreme Learning Machine based on sparse coding

A Pruning Algorithm for Extreme Learning Machine based on Sp...

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International Joint Conference on Neural Networks

作者： Yuanlong Yu Zhenzhen Sun College of Mathematics and Computer Science Fuzhou University

ISBN: (纸本)9781509006212

This paper presents a pruned sparse extreme learning machine (PS-ELM) algorithm, which can generate a compact single-hidden-layer neural network (SLNN) by automatically pruning the number of hidden nodes while keep high accuracy. In this PS-ELM algorithm, input connections between input and hidden layers are base vectors, which can sparsely map the input features into hidden layer by using gradient projection (GP) algorithm;The output weights between hidden and output layers can map the sparse features into class labels. This PS-ELM algorithm initializes the SLNN given superfluous number of hidden nodes. The subsequent training process consists of four iterative steps. The first one is to update base vectors by using Lagrange dual optimization. The second one is to prune zero base vectors which are considered to be insignificant. The third one is sparse coding which re-encodes the training samples given remained base vectors. The fourth one is to update output weight matrix by using ELM-like algorithm. This iterative process stops once the number of hidden nodes at current time is equal to the number at the last time. This PS-ELM algorithm can improve the sparsity and distinction of hidden layer feature representations. Mean-while, the pruning is independent on hand-designed threshold. Experimental results on benchmark datasets have shown that the PS-ELM algorithm can automatically achieve a reasonable compact network structure while keep comparable or much higher accuracy in classification.

关键词： sparse coding Learning machines base vector tree surgery algorithms Alkali Base of a number system Hide AUTOMATIC Bases Base

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Select-and-Sample for Spike-and-Slab sparse coding 16

Select-and-Sample for Spike-and-Slab Sparse Coding

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Annual Conference on Neural Information Processing Systems

作者： Abdul-Saboor Sheikh Joerg Luecke Technical University of Berlin Germany and Cluster of Excellence Hearing4all University of Oldenburg Germany and SAP Innovation Center Network Berlin Research Center Neurosensory Science and Cluster of Excellence Hearing4all and Dept. of Medical Physics and Acoustics University of Oldenburg Germany

ISBN: (纸本)9781510838819

Probabilistic inference serves as a popular model for neural processing. It is still unclear, however, how approximate probabilistic inference can be accurate and scalable to very high-dimensional continuous latent spaces. Especially as typical posteriors for sensory data can be expected to exhibit complex latent dependencies including multiple modes. Here, we study an approach that can efficiently be scaled while maintaining a richly structured posterior approximation under these conditions. As example model we use spike-and-slab sparse coding for V1 processing, and combine latent subspace selection with Gibbs sampling (select-and-sample). Unlike factored variational approaches, the method can maintain large numbers of posterior modes and complex latent dependencies. Unlike pure sampling, the method is scalable to very high-dimensional latent spaces. Among all sparse coding approaches with non-trivial posterior approximations (MAP or ICA-like models), we report the largest-scale results. In applications we firstly verify the approach by showing competitiveness in standard denoising benchmarks. Secondly, we use its scalability to, for the first time, study highly-overcomplete settings for V1 encoding using sophisticated posterior representations. More generally, our study shows that very accurate probabilistic inference for multi-modal posteriors with complex dependencies is tractable, functionally desirable and consistent with models for neural inference.

关键词： Bayesian inference sparse coding Scalability Large Numbers

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Recognition of Low-Resolution Face Images using sparse coding of Local Features

Recognition of Low-Resolution Face Images using Sparse Codin...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference

作者： M. Saad Shakeel Kin-Man-Lam Centre for Signal Processing Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781509024018

In this paper, we propose a new approach for recognition of low-resolution face images by using sparse coding of local features. The proposed algorithm extracts Gabor features from a low-resolution gallery image and a query image at different scales and orientations, then projects the features separately into a new low-dimensional feature space using sparse coding that preserves the sparse structure of the local features. To determine the similarity between the projected features, a coefficient vector is estimated by using linear regression that determines the relationship between the projected gallery and query features. On the basis of this coefficient vector, residual values will be computed to classify the images. To validate our proposed method, experiments were performed using three databases (ORL, Extended-Yale B, and CAS-PEAL-R1), which contain images with different facial expressions and lighting conditions. Experimental results show that our method outperforms various classical and state-of-the-art face recognition methods.

关键词： Recognition Low-Resolution Face Images sparse coding Local Features

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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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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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Feature Extraction of Vibration Signals Based on Fusion sparse coding

Feature Extraction of Vibration Signals Based on Fusion Spar...

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The 29th International Congress on Condition Monitoring and Diagnostic Engineering Management

作者： Gang Tang Yao Zhang Huaqing Wang College of Mechanical and Electrical Engineering Beijing University of Chemical Technology

ISBN: (纸本)9781510868434

Feature extraction of vibration signals plays an important role in the machine fault *** coding(SC) provides an effective way to make ***,SC cannot accurately extract impulse components and identify fault character *** addition,it may remove some useful information of the *** improve the performance of SC,this paper uses fusion sparse coding to extract the fault features of vibration signals,which fuses vibration signals from different sensors to acquire better sparse ***,a vibration signal is processed by several kinds of SC in ***,the fusion scheme is developed to get new sparse *** vibration signals generated by a roller bearing with an inner-race fault are employed to validate the effectiveness of the proposed method and the results show that the fusion sparse coding outperforms single sparse coding.

关键词： Vibration signals feature extraction sparse coding information fusion

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Non-negativity and dependence constrained sparse coding for image classification

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2015年第1期26卷 247-254页

作者： Han, Hong Liu, Sanjun Gan, Lu Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

sparse coding method is a powerful tool in many computer vision applications. However, due to the combinatorial optimization of sparse coding involving both additive and subtractive interactions, the features can cancel each other out with subtraction. And also, in the process of independent coding, the locality and the similarity among the instances which be encoded may be lost. To solve these problems, an image classification framework by leveraging the Non-negative Matrix Factorization and graph Laplacian techniques is presented. Firstly, the Non-negative Matrix Factorization is used to constrain both of the codebook and the corresponding coding coefficients non-negativity. To preserve the dependence properties of the locality and the similarity among the instances, the graph Laplacian regularization is utilized. Then, along with max pooling and spatial pyramid matching, we extend our method to Bag-of-Words image representation. Finally, the linear SVM is leveraged for image 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： Non-negative Matrix Factorization Graph Laplacian Dictionary learning sparse coding Spatial pyramid Image classification Parameter selection sparseness

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