检索结果-内蒙古大学图书馆

Sample selection for visual domain adaptation via sparse coding

SIGNAL PROCESSING-IMAGE COMMUNICATION 2016年 44卷 92-100页

作者： Li, Xiao Fang, Min Zhang, Ju-Jie Wu, Jinqiao Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

Visual domain adaptation has attracted much attention and has made great achievement in recent years. It deals with the problem of distribution divergence between source and target domains. Current methods mostly focus on transforming images from different domains into a common space to minimize the distribution divergence. However, there are many irrelevant source samples for target domain even after the transformation. In order to eliminate the irrelevant samples, we develop a sample selection algorithm using sparse coding theory. We do the sample selection in a common subspace of source and target data to find as many as relevant source samples. In the common subspace, data characteristics are preserved by using graph regularization. Therefore, we can select the most relevant samples for our target image classification task. Moreover, in order to build a discriminative classifier for the target domain, we use not only the common part of source and target domains learned in the common subspace but also the specific part of target domain. The algorithm can be extended to handle samples from multiple source domains. Experimental results show that our visual domain adaptation method on the image classification tasks can be very effective for the state-of-the-art datasets. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Image classification Visual domain adaptation Sample selection sparse coding L-2,L-1 norm

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Underdetermined Blind Source Separation Using sparse coding

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2017年第12期28卷 3102-3108页

作者： Zhen, Liangli Peng, Dezhong Yi, Zhang Xiang, Yong Chen, Peng Sichuan Univ Coll Comp Sci Machine Intelligence Lab Chengdu 610065 Sichuan Peoples R China Deakin Univ Sch Engn & Informat Technol Geelong Vic 3217 Australia

In an underdetermined mixture system with n unknown sources, it is a challenging task to separate these sources from their m observed mixture signals, where m < n. By exploiting the technique of sparse coding, we propose an effective approach to discover some 1-D subspaces from the set consisting of all the time-frequency (TF) representation vectors of observed mixture signals. We show that these 1-D subspaces are associated with TF points where only single source possesses dominant energy. By grouping the vectors in these subspaces via hierarchical clustering algorithm, we obtain the estimation of the mixing matrix. Finally, the source signals could be recovered by solving a series of least squares problems. Since the sparse coding strategy considers the linear representation relations among all the TF representation vectors of mixing signals, the proposed algorithm can provide an accurate estimation of the mixing matrix and is robust to the noises compared with the existing underdetermined blind source separation approaches. Theoretical analysis and experimental results demonstrate the effectiveness of the proposed method.

关键词： Mixing matrix identification single source detection source recovery sparse coding underdetermined blind source separation (UBSS)

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Device-free indoor localization based on sparse coding with nonconvex regularization and adaptive relaxation localization criteria

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2023年第2期14卷 429-443页

作者： Zhang, Kangkang Tan, Benying Ding, Shuxue Li, Yujie Li, Guangwei Guilin Univ Elect Technol Guilin Peoples R China

Considering practical device-free localization (DFL), the localization precision is usually proportional to the sensor density, with an ordinary arrangement. A better localization performance generally indicates a higher density of the sensor nodes that need to be deployed. To overcome this problem, we propose a new framework for dispatching sensor nodes. Regarding this framework, only few sensor nodes are applied to achieve an excellent localization performance. We consider the DFL problem as a sparse coding problem. To maintain the convexity of the cost function for more accurate solutions, we introduce the generalized minimax-concave (GMC) regularization to approximate the l(0)-norm regularization. The global optimal solution can be identified by adopting the forward-backward splitting algorithm (FBS). Furthermore, the localization error is further decreased by the proposed adaptive relaxation localization (ARL) criteria for target localization. We tackled two experimental scenes in a real laboratory and compared the performance of the proposed algorithm with that of other algorithms using different regularizations. The experimental results show 100% grid localization accuracy under the 0.5 x 0.5 m grid scene. After adopting the ARL criteria, the average localization error decreased from 0.098 to 0.053 m in the 0.25 x 0.25 m grid scene, with an increased rate of 45.9%. This is the best performance compared to state-of-the-art framework.

关键词： DFL Frameworks sparse coding Generalized minimax-concave (GMC) Adaptive relaxation localization (ARL)

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sparse coding based Robust Image Denoising via Coupled Dictionary 1

Sparse Coding based Robust Image Denoising via Coupled Dicti...

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1st India International Conference on Information Processing (IICIP)

作者： Singh, Kuldeep Viswakarma, D. K. Walia, Gurjit S. Kapoor, Rajiv Bharat Elect Ltd Cent Res Lab Ghaziabad India Delhi Technol Univ Dept Elect & Commun Delhi India Minist Def DRDO Delhi India

ISBN: (纸本)9781467369848

This paper addresses the image denoising problem using the recent advancements in sparse representation based image restoration. This novel approach is based on sparse coding using a pre-learned coupled dictionary. Prior research suggests that it is possible to represent every image patch as a sparse linear combination of dictionary elements learned from a set of example patches. Joint training of coupled dictionaries for clear and noisy images helps to enforce self-similarity between noisy and latent images. The sparsity measure over such a coupled dictionary is used as a regularisation constraint to recover a denoised image patch from a noisy image patch. The experimental results indicate that the proposed method appears to be competitive with other denoising methods.

关键词： sparse coding Coupled dictionary Denoising

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sparse coding based airport detection from medium resolution Landsat-7 satellite remote sensing images 3

Sparse coding based airport detection from medium resolution...

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3rd International Workshop on Earth Observation and Remote Sensing Applications (EORSA)

作者： Cheng, Gong Han, Junwei Zhou, Peicheng Yao, Xiwen Zhang, Dingwen Guo, Lei Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China

ISBN: (纸本)9781479941841

A simple but effective method for airport detection from medium resolution Landsat-7 satellite remote sensing images based on sparse coding is presented. It consists of three phases: dictionary construction, sparse coding, and airport detection. Firstly, an over-complete dictionary is constructed using a set of airport training samples. Secondly, test images are scanned using multi-scale windows and each scanned window is sparsely coded in terms of atoms of the dictionary. Finally, sparsity concentration index of each scanned window is calculated based on the coding coefficients, which is used to decide the airport detection. Evaluations on publically available satellite images and comparisons with state-of-the-art approaches have demonstrated the superiority of the presented work.

关键词： airport detection Landsat-7 satellite images sparse coding multi-scale

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sparse coding-BASED SPATIOTEMPORAL SALIENCY FOR ACTION RECOGNITION

SPARSE CODING-BASED SPATIOTEMPORAL SALIENCY FOR ACTION RECOG...

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IEEE International Conference on Image Processing (ICIP)

作者： Zhang, Tao Xu, Long Yang, Jie Shi, Pengfei Jia, Wenjing Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200240 Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW 2007 Australia

ISBN: (纸本)9781479983391

In this paper, we address the problem of human action recognition by representing image sequences as a sparse collection of patch-level spatiotemporal events that are salient in both space and time domain. Our method uses a multi-scale volumetric representation of video and adaptively selects an optimal space-time scale under which the saliency of a patch is most significant. The input image sequences are first partitioned into non-overlapping patches. Then, each patch is represented by a vector of coefficients that can linearly reconstruct the patch from a learned dictionary of basis patches. We propose to measure the spatiotemporal saliency of patches using Shannon's self-information entropy, where a patch's saliency is determined by information variation in the contents of the patch's spatiotemporal neighborhood. Experimental results on two benchmark datasets demonstrate the effectiveness of our proposed method.

关键词： sparse coding spatiotemporal saliency action recognition Shannon information entropy

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sparse coding AND DICTIONARY LEARNING BASED ON THE MDL PRINCIPLE

SPARSE CODING AND DICTIONARY LEARNING BASED ON THE MDL PRINC...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

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

ISBN: (纸本)9781457705397

The power of sparse signal coding 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. This work aims at filling this gap by means of the Minimum Description Length (MDL) principle - a well established information-theoretic approach to statistical inference. The resulting framework derives a family of efficient sparse coding and modeling (dictionary learning) algorithms, which by virtue of the MDL principle, are completely parameter free. Furthermore, such framework allows to incorporate additional prior information in the model, such as Markovian dependencies, in a natural way. We demonstrate the performance of the proposed framework with results for image denoising and classification tasks.

关键词： sparse coding dictionary learning MDL denoising classification

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Single-image super resolution using evolutionary sparse coding technique

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IET IMAGE PROCESSING 2017年第1期11卷 13-21页

作者： Ahmadi, Kaveh Salari, Ezzatollah Univ Toledo Dept Elect Engn & Comp Engn 2801 W Bancroft St Toledo OH 43606 USA

sparse coding (SC) has recently become a widely used tool in signal and image processing. The sparse linear combination of elements from an appropriately chosen over-complete dictionary can represent many signal patches. SC applications have been explored in many fields such as image super resolution (SR), image-feature extraction, image reconstruction, and segmentation. In most of these applications, learning-based SC has provided an excellent image quality. SC involves two steps: dictionary construction and searching the dictionary using quadratic programming. This study focuses on the searching step and a new adaptive variation of genetic algorithm is proposed to search and find the optimum closest match in the dictionary. Also, inspired by the proposed evolutionary SC (ESC), a single-image SR algorithm is proposed. A sparse representation for each patch of the low-resolution input image is obtained by ESC and it is used to generate the high-resolution output image. Experimental results show that the proposed ESC-based method would lead to a better SR image quality.

关键词： image coding learning (artificial intelligence) quadratic programming genetic algorithms search problems image resolution evolutionary SC technique sparse coding image processing sparse linear combination over-complete dictionary signal patch representation learning-based SC image quality dictionary construction dictionary search quadratic programming adaptive variation genetic algorithm optimum closest match ESC single-image SR algorithm sparse representation low-resolution input image patch high-resolution output image generation

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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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Towards Robustness and Efficiency of Coherence-Guided Complex Convolutional sparse coding for Interferometric Phase Restoration

IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2024年 10卷 690-699页

作者： Ding, Xiang Kang, Jian Bai, Yusong Zhang, Anping Liu, Jialin Yokoya, Naoto Soochow Univ Sch Elect & Informat Engn Suzhou 215006 Peoples R China Alibaba Grp US DAMO Acad Bellevue WA 98004 USA Univ Tokyo Grad Sch Frontier Sci Tokyo 1138654 Japan RIKEN Ctr Adv Intelligence Project AIP Geoinformat Unit Kashiwa 2778561 Japan

Recently, complex convolutional sparse coding (ComCSC) has demonstrated its effectiveness in interferometric phase restoration, owing to its prominent performance in noise mitigation and detailed phase preservation. By incorporating the estimated coherence into ComCSC as prior knowledge for re-weighting individual complex residues, coherence-guided complex convolutional sparse coding (CoComCSC) further improves the quality of restored phases, especially over heterogeneous land-covers with rapidly varying coherence. However, due to the exploited L-2 norm of the data fidelity term, the original CoComCSC is not robust to outliers when relatively low coherence values are sparsely distributed over high ones. We propose CoComCSC-L1 and CoComCSC-Huber to improve the robustness of CoComCSC based on the L-1 and Huber norms. Moreover, we propose an efficient solver to decrease the computational cost of solving the linear system subproblem within ComCSC-based optimization problems. By comparing the proposed methods to other state-of-the-art methods using both simulated and real data, the proposed methods demonstrate their effectiveness. Additionally, the proposed solver has the potential to improve optimization speed by approximately 10% compared to the state-of-the-art solver.

关键词： Noise Image restoration Filtering Coherence Encoding Convolution Noise reduction SAR SAR interferometry (InSAR) sparse coding Convolutional dictionary learning

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