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检索条件"主题词=sparse coding"

共 2101 条记录，以下是2031-2040 订阅

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DICTIONARY LEARNING FOR A sparse APPEARANCE MODEL IN VISUAL TRACKING

DICTIONARY LEARNING FOR A SPARSE APPEARANCE MODEL IN VISUAL ...

引用

IEEE International Conference on Image Processing

作者： Sylvain Rousseau Pierre Chainais Christelle Garnier Ecole Centrale de Lille CRIStAL UMR CNRS 9189 Institut Mines-Telecom/Telecom Lille CRIStAL UMR CNRS 9189

ISBN: (纸本)9781479983407

This paper presents a novel approach to visual object tracking based on particle filtering. The tracked object is modelled by a sparse representation provided by dictionary learning. Such an approach permits to describe the target by a model of reduced dimension. The likelihood of a candidate region is built on a similarity measure between the sparse representations of a set of patches (at known positions) in the dictionary learnt from the reference template. Experimental validation is performed on various video sequences and shows the robustness of the proposed approach.

关键词： Dictionary learning sparse coding Particle filtering Object tracking Particle filters sparse coding visual tracking tracking objects sparse representation Video sequences similar measure Dictionaries as Topic

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Parallel Optimization of K-SVD Algorithm for Image Denoising Based on Spark

Parallel Optimization of K-SVD Algorithm for Image Denoising...

引用

2016 IEEE 13th International Conference on Signal Processing（ICSP2016）

作者： Tan Chao Wei Zhihui Wu Ze Bin Chen Yufeng Gu Jingping School of Science Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing University of Science and Technology

With the development of society, the image size and resolution are gradually increasing. At the same time, the fast image processing is required. The K-SVD algorithm is an iterative method that alternates between sparse coding and updating the overcomplete dictionary. It is a very simple and highly effective method for the signal of sparse representation. However, the K-SVD algorithm is a computationally intensive algorithm. The traditional computing model will take a lot of time to complete the calculation. As the next generation computing model, Spark not only has a strong computing power, but also has a memory-based fast processing capacity. In order to process image denoising efficiently, this paper proposes a implementation of distributed parallel optimization of K-SVD algorithm(K-SVD-P) on Spark. The results showed that K-SVDP not only has a good speed-up ratio, but also retains the image texture and other details.

关键词： Spark image denoising K-SVD sparse coding updating dictionary distributed parallel optimization K-SVD-P

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GROUP SPARSITY RESIDUAL WITH NON-LOCAL SAMPLES FOR IMAGE DENOISING

GROUP SPARSITY RESIDUAL WITH NON-LOCAL SAMPLES FOR IMAGE DEN...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Zhiyuan Zha Xinggan Zhang Qiong Wang Yechao Bai Lan Tang Xin Yuan School of Electronic Science and Engineering Nanjing University Nanjing 210023 China Nokia Bell Labs 600 Mountain Avenue Murray Hill NJ 07974 USA

ISBN: (纸本)9781538646595

Inspired by group-based sparse coding, recently proposed group sparsity residual (GSR) scheme has demonstrated superior performance in image processing. However, one challenge in GSR is to estimate the residual by using a proper reference of the group-based sparse coding (GSC), which is desired to be as close to the truth as possible. Previous researches utilized the estimations from other algorithms (i.e., GMM or BM3D), which are either not accurate or too slow. In this paper, we propose to use the Non-Local Samples (NLS) as reference in the GSR regime for image denoising, thus termed GSR-NLS. More specifically, we first obtain a good estimation of the group sparse coefficients by the image nonlocal self-similarity, and then solve the GSR model by an effective iterative shrinkage algorithm. Experimental results demonstrate that the proposed GSR-NLS not only outperforms many state-of-the-art methods, but also delivers the competitive advantage of speed.

关键词： Image denoising group-based sparse coding group sparsity residual nonlocal self-similarity iterative shrinkage algorithm image denoising Galvanic Skin Response images RESIDUAL Reference Groups sparse coding Gm(m) competitive advantage truth Imagery (Psychotherapy)

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NON-RIGID 3D SHAPE RECOGNITION VIA DICTIONARY LEARNING

NON-RIGID 3D SHAPE RECOGNITION VIA DICTIONARY LEARNING

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Yin Zhou Kai Liu Kenneth E. Barner University of Delaware Newark DE USA 19716 School of Electrical Engineering and Information Sichuan University China

ISBN: (纸本)9781479903573

Non-rigid 3D shape recognition is an important and challenging research topic in computer vision and pattern recognition. This paper presents a novel algorithm, called dictionary learning based on supervised locally linear representation (DL-SLLR), for efficient 3D shape recognition using shape descriptors. Specifically, we introduce a novel locality-preservation error term along with a label approximation error term into the objective function. The proposed algorithm optimizes a dictionary for its capability in representation as well as its locality-preservation capability, which thus allows more consistent encoding of similar descriptors compared with sparse coding. In addition, the proposed SLLR coding yields a closed-form solution, compared to many sparse coding algorithms. Experimental results demonstrate that using majority voting, DL-SLLR outperforms D-KSVD and SVM over a newly generated SLI 3D Face Dataset and the SHREC'11 Contest Dataset.

关键词： Shape recognition Point cloud classification Dictionary learning Classification sparse coding

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Encoding sparse Features in a Bidirectional Associative Memory

Encoding Sparse Features in a Bidirectional Associative Memo...

引用

International Joint Conference on Neural Networks

作者： Nareg Berberian Zoya Aamir Sebastien Helie Sylvain Chartier School of Psychology 136 Jean-Jacques Lussier University of Ottawa Department of Psychological Sciences 703 Third Purdue University

ISBN: (纸本)9781509006212

Bidirectional Associative Memories (BAMs) are artificial neural networks that can learn and recall various types of associations. Although BAM models have shown great promise at modeling human cognitive processes, these models have often been investigated under conditions where stimuli are densely represented using a bipolar coding scheme. However, research has shown that dense representations are energetically costly given that various stimulus representations need to be detected, processed and analyzed on a daily basis. Instead, biological networks work on minimizing energy expenditure by encoding sparse stimulus features that maximize information representation. This paper extends this line of search and shows that BAM models can improve learning and recall performance in a sparse encoding regime. It provides a strategy for artificial neural networks that seek to maintain valuable processing resources, especially under constraints of noisy representations of stimulus features.

关键词： Artificial intelligence Connectionist models Bidirectional associative memory sparse coding

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DENOISING OF IMAGE PATCHES VIA sparse REPRESENTATIONS WITH LEARNED STATISTICAL DEPENDENCIES

DENOISING OF IMAGE PATCHES VIA SPARSE REPRESENTATIONS WITH L...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Tomer Faktor Yonina C. Eldar Michael Elad Departments of Electrical Engineering and Computer Science Technion — Israel Institute of Technology Haifa 32000 Israel

ISBN: (纸本)9781457705380

We address the problem of denoising for image patches. The approach taken is based on Bayesian modeling of sparse representations, which takes into account dependencies between the dictionary atoms. Following recent work, we use a Boltzman machine to model the sparsity pattern. In this work we focus on the special case of a unitary dictionary and obtain the exact MAP estimate for the sparse representation using an efficient message passing algorithm. We present an adaptive model-based scheme for sparse signal recovery, which is based on sparse coding via message passing and on learning the model parameters from the data. This adaptive approach is applied on noisy image patches in order to recover their sparse representations over a fixed unitary dictionary. We compare the denoising performance to that of previous sparse recovery methods, which do not exploit the statistical dependencies, and show the effectiveness of our approach.

关键词： sparse representations MAP Boltzmann ma-chine unitary dictionary message passing image denoising Noise reduction images sparse representation Signal restoration message passing Dictionaries as Topic representations Maximum a posteriori estimation Noisy Image Patches sparse coding Unitary

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Combining sparse NMF with deep neural network: A new classification-based approach for speech enhancement

Combining sparse NMF with deep neural network: A new classif...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： H. W. Tseng M. Hong Z. Q. Luo Electr. & Comput. Eng. Univ. of Minnesota Minneapolis MN USA

ISBN: (纸本)9781467369985

In this work, we consider enhancing a target speech from a single-channel noisy observation corrupted by non-stationary noises at low signal-to-noise ratios (SNRs). We take a classification-based approach, where the objective is to estimate an Ideal Binary Mask (IBM) that classifies each time-frequency (T-F) unit of the noisy observation into one of the two categories: speech-dominant unit or noise-dominant unit. The estimated mask is used to binary weight the noisy mixture to obtain the enhanced speech. In the proposed system, the sparse non-negative matrix factorization (NMF) is used to extract features from the noisy observation, followed by a Deep Neural Network (DNN) for classification. Compared with several existing classification-based systems, the proposed system uses minimal speech-specific domain knowledge, but is able to achieve better performance in certain low SNR regions. Moreover, the proposed system outperforms the traditional statistical method, especially in terms of improving the intelligibility.

关键词： Speech enhancement deep neural network (DNN) non-negative matrix factorization (NMF) sparse coding Speech enhancement sparse coding Noise NMF protocol Neural network domain knowledge

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Research on Fault Diagnosis Method of Coolant System in Nuclear Power Plant

Research on Fault Diagnosis Method of Coolant System in Nucl...

引用

作者： Qiu Pinda Ye Liqiang Shandong Nuclear Power Company

Coolant system is one of the most important systems in nuclear power plant. The safety and stability of the system is the basis of the stable operation of the whole nuclear power unit. Firstly, the typical fault characteristics of the system are introduced. In order to improve the accuracy of fault diagnosis, an expert diagnosis method based on sparse coding is proposed by fusing the sparse coding fault diagnosis results with the expert system. The results show that the method can extract fault feature information effectively. The accuracy of expert system for fault diagnosis of coolant system is 87.5%. After sparse coding fusion, the accuracy of fault diagnosis is increased to 94.4%. The validity and reliability of the system are verified, which can provide reference for safe operation and decision-making of nuclear power plant.

关键词： Coolant system Fault diagnosis Expert system sparse coding

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LABEL CONSISTENT RECURSIVE LEAST SQUARES DICTIONARY LEARNING FOR IMAGE CLASSIFICATION

LABEL CONSISTENT RECURSIVE LEAST SQUARES DICTIONARY LEARNING...

引用

IEEE International Conference on Image Processing

作者： Sergio Matiz Kenneth E. Barner Department of Electrical and Computer Engineering University of Delaware

ISBN: (纸本)9781467399623

A label consistent recursive least squares dictionary learning algorithm, LC-RLSDLA, is proposed to learn discriminative dictionaries for image classification based on sparse coding. The class label information and a label consistency term are used in the cost function to enforce discriminability among the sparse codes. Two operation modes are derived for the LC-RLSDLA: the supervised learning mode, in which the algorithm employs a training set to learn the dictionary and linear classifier simultaneously, and the online semi-supervised learning mode, in which the algorithm continuously learns as it classifies new vectors. Experiments performed on two face recognition databases demonstrate that the proposed method outperforms state-of-the-art sparse coding algorithms.

关键词： Image classification sparse coding Dictionary learning Supervised learning

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Prediction of O-Glycosylation Sites in Protein Sequence by Kernel Principal Component Analysis

Prediction of O-Glycosylation Sites in Protein Sequence by K...

引用

International Conference on Computational Aspects of Social Networks(国际社会网络计算会议 CASoN 2010)

作者： Xue-mei Yang Xue-wei Cui Xue-zhu Yang College of Mathematics and Information Science Xianyang Normal University Xianyang China Department of Applied Mathematics Northwestern Polytechnical Univ. Xi'an China Agricultural Technology Generalization Station of Shangluo Shangluo China

O-glycosylation is one of the main types of the mammalian protein glycosylation, it occurs on the particular site of serine and threonine. It's important to predict the O-glycosylation site. In this paper, we propose a new method of kernel principal component analysis(KPCA) to predict the Oglycosylation site with window size w=9. The samples for experiment are encoded by the sparse coding and projected into kernel space first, then the features are extracted by PCA, at last the classification is done by Mahanalobis distance. The result of experiments shows that the proposed method of KPCA is more effective and accurate than PCA. The prediction accuracy is about 84.5％.

关键词： prediction glycosylation protein sparse coding KPCA classification

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