检索结果-内蒙古大学图书馆

sparse coding and dictionary learning with class-specific group sparsity

NEURAL COMPUTING & APPLICATIONS 2018年第4期30卷 1265-1275页

作者： Sun, Yuping Quan, Yuhui Fu, Jia South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Guangdong Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China South China Univ Technol Sch Journalism & Commun Guangzhou 510006 Guangdong Peoples R China

In recent years, sparse coding via dictionary learning has been widely used in many applications for exploiting sparsity patterns of data. For classification, useful sparsity patterns should have discrimination, which cannot be well achieved by standard sparse coding techniques. In this paper, we investigate structured sparse coding for obtaining discriminative class-specific group sparsity patterns in the context of classification. A structured dictionary learning approach for sparse coding is proposed by considering the norm on each class of data. An efficient numerical algorithm with global convergence is developed for solving the related challenging minimization problem. The learned dictionary is decomposed into class-specific dictionaries for the classification that is done according to the minimum reconstruction error among all the classes. For evaluation, the proposed method was applied to classifying both the synthetic data and real-world data. The experiments show the competitive performance of the proposed method in comparison with several existing discriminative sparse coding methods.

关键词： Structured sparsity group sparse coding Discriminative dictionary learning Classification

来源：评论

学校读者我要写书评

暂无评论

FACE RECOGNITION USING HOG FEATURE AND group sparse coding

FACE RECOGNITION USING HOG FEATURE AND GROUP SPARSE CODING

引用

20th IEEE International Conference on Image Processing (ICIP)

作者： Li, Yuhua Qi, Chun Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian Shaanxi Peoples R China

ISBN: (纸本)9781479923410

Standard sparsity concept mainly focuses on the sparsity of coefficient vector while other important characteristics are less considered. For example, given a structured dictionary, some structured patterns are more likely to occur than the others. In this paper, a face recognition method using group sparse coding is presented. Training samples of the same class are gathered to form a sub-dictionary and a group structured dictionary is obtained by concentrating sub-dictionaries of all classes. We decompose each testing sample as a product of the group structured dictionary and a group sparse coefficient vector. Finally, recognition is accomplished by evaluating which class of training samples leads to the minimum reconstruction error. For better invariance to illumination and expression changes, histogram of oriented gradients (Hog) feature is extracted to represent face image. Experimental results on benchmark face databases show the proposed method leads to higher recognition rates and shorter recognition time.

关键词： Standard sparsity face recognition Hog feature group sparse coding

来源：评论

学校读者我要写书评

暂无评论

Image Restoration via Reconciliation of group Sparsity and Low-Rank Models

引用

IEEE TRANSACTIONS ON IMAGE PROCESSING 2021年 30卷 5223-5238页

作者： Zha, Zhiyuan Wen, Bihan Yuan, Xin Zhou, Jiantao Zhu, Ce Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Nokia Bell Labs Murray Hill NJ 07974 USA Univ Macau State Key Lab Internet Things Smart City Macau 999078 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

Image nonlocal self-similarity (NSS) property has been widely exploited via various sparsity models such as joint sparsity (JS) and group sparse coding (GSC). However, the existing NSS-based sparsity models are either too restrictive, e.g., JS enforces the sparse codes to share the same support, or too general, e.g., GSC imposes only plain sparsity on the group coefficients, which limit their effectiveness for modeling real images. In this paper, we propose a novel NSS-based sparsity model, namely, low-rank regularized group sparse coding (LR-GSC), to bridge the gap between the popular GSC and JS. The proposed LR-GSC model simultaneously exploits the sparsity and low-rankness of the dictionary-domain coefficients for each group of similar patches. An alternating minimization with an adaptive adjusted parameter strategy is developed to solve the proposed optimization problem for different image restoration tasks, including image denoising, image deblocking, image inpainting, and image compressive sensing. Extensive experimental results demonstrate that the proposed LR-GSC algorithm outperforms many popular or state-of-the-art methods in terms of objective and perceptual metrics.

关键词： Image restoration Image coding Dictionaries Adaptation models Minimization Image denoising sparse matrices group sparse coding low-rank regularized group sparse coding alternating minimization adaptive parameter adjustment image restoration

来源：评论

学校读者我要写书评

暂无评论

FACE RECOGNITION USING HOG FEATURE AND group sparse coding

FACE RECOGNITION USING HOG FEATURE AND GROUP SPARSE CODING

引用

IEEE International Conference on Image Processing

作者： Yuhua Li Chun Qi The School of Electronic and Information Engineering Xi'an Jiaotong University China

ISBN: (纸本)9781479923427

关键词： Standard sparsity face recognition Hog feature group sparse coding Facial Recognition Dictionaries as Topic reconstruction error Face Face recognition techniques Recognition (Psychology) sparse coding illumination Lighting

来源：评论

学校读者我要写书评

暂无评论

RECONCILIATION OF group SPARSITY AND LOW-RANK MODELS FOR IMAGE RESTORATION

RECONCILIATION OF GROUP SPARSITY AND LOW-RANK MODELS FOR IMA...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Zha, Zhiyuan Wen, Bihan Yuan, Xin Zhou, Jiantao Zhu, Ce Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Nokia Bell Labs 600 Mt Ave Murray Hill NJ 07974 USA Univ Macau Dept Comp & Informat Sci Macau 999078 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

ISBN: (纸本)9781728113319

Image nonlocal self-similarity (NSS) property has been widely exploited via various sparsity models such as joint sparsity (JS) and group sparse coding (GSC). However, the existing NSS-based sparsity models are either too restrictive, i.e., JS enforces the sparse codes to share the same support, or too general, i.e., GSC imposes only plain sparsity on the group coefficients, which limit their effectiveness for modeling real images. In this paper, we propose a novel NSS-based sparsity model, namely low-rank regularized group sparse coding (LR-GSC), to bridge the gap between the popular GSC and JS. The proposed LR-GSC model simultaneously exploits the sparsity and low-rankness of the dictionary-domain coefficients for each group of similar patches. To make the proposed scheme tractable and robust, an alternating minimization with an adaptive adjusted parameter strategy is developed to solve the proposed optimization problem. Experimental results on both image deblocking and denoising demonstrate that the proposed LR-GSC image restoration algorithms outperform many popular or state-of-the-art methods, in terms of both the objective and perceptual quality.

关键词： group sparse coding low-rank regularized group sparse coding alternating minimization adaptive parameter adjustment image restoration

来源：评论

学校读者我要写书评

暂无评论

Face Recognition Using Simultaneous Discriminative Feature and Adaptive Weight Learning Based on group sparse Representation

引用

IEEE SIGNAL PROCESSING LETTERS 2019年第3期26卷 390-394页

作者： Du, Lingshuang Hu, Haifeng Sun Yat Sen Univ Sch Elect & Informat Engn Guangzhou 510006 Guangdong Peoples R China

To better address face recognition task with occlusions and illumination changes, this letter proposes a novel framework called simultaneous discriminative feature and adaptive weight learning (SDFAWL). Sepecifically, SDFAWL uses a novel unified objective function to simultaneously learn the discriminant features, adaptive feature weights, and classification coefficients. First, a discriminant feature projection is incorporated into group sparse representation model, which reduces the intra-class distance between training samples. Second, we integrate adaptive feature weights into our model to penalize the noisy pixels, which is simultaneously learned by our unified objective function. Besides, we derive an efficient algorithm to optimize the proposed objective function, where the simultaneously learning scheme can encourage obtained parameters combining better and decrease the information loss. Extensive experiments under different conditions including occlusion, random noise, and illumination changes are conducted on the famous Aleix Martinez and Robert Benavente and ExYale B database demonstrate the effectiveness of our model.

关键词： Discriminative feature learning adaptive feature weights group sparse coding face recognition

来源：评论

学校读者我要写书评

暂无评论

LOW-RANK, sparse MATRIX DECOMPOSITION AND group sparse coding FOR IMAGE CLASSIFICATION

LOW-RANK, SPARSE MATRIX DECOMPOSITION AND GROUP SPARSE CODIN...

引用

19th IEEE International Conference on Image Processing (ICIP)

作者： Zhang, Lihe Ma, Chen Dalian Univ Technol Sch Informat & Commun Engn Liaoning 116023 Peoples R China

ISBN: (纸本)9781467325332;9781467325349

This paper presents a novel image classification framework (referred to as LR-GSC) by leveraging the low-rank, sparse matrix decomposition and group sparse coding. First, motivated by the observation that local features (such as SIFT) extracted from neighboring patches in an image usually contain correlated (or common) items and specific (or noisy) items, we decompose the local features matrix of an image into a low-rank matrix and a sparse matrix. Second, we train the group sparse dictionaries on the low-rank parts and sparse parts respectively. And then, the dictionaries of the two parts are jointed to encode the original SIFT features by group coding. Finally, linear SVM classifier is used for the classification. The method is tested on the Caltech-101 dataset and UIUC-sports dataset, and achieves competitive or better results than the state-of-the-art methods.

关键词： image classification low-rank and sparse matrix decomposition group sparse coding

来源：评论

学校读者我要写书评

暂无评论

An overview of sparse representation based action recognition in video 2

An overview of sparse representation based action recognitio...

引用

2nd International Conference on Computer, Communication, and Signal Processing (ICCCSP) - Special Focus on Technology and Innovation for Smart Environment

作者： Ushapreethi, P. Lakshmipriya, G. G. VIT Univ Sch Informat Technol & Engn Vellore Tamil Nadu India

ISBN: (纸本)9781538611418

Most of the action recognition researchers are undergoing intense study on sparse representation, due to its discriminative nature. The basis of the sparse representation is sparse coding, which give way for creating sparse dictionaries. The aim of this paper is to show the details about action recognition in video;the basics of sparse representation and its usage in action recognition;advancements in sparse representation;practical issues of recent sparse representation methods and future possibilities to solve such issues. This paper also provides various models and techniques used for the sparse representation based action recognition.

关键词： sparse representation action recognition locality coding group sparse coding sparse dictionaries

来源：评论

学校读者我要写书评

暂无评论

Structured sparse representation for human action recognition

引用

NEUROCOMPUTING 2015年 161卷 38-46页

作者： Moayedi, F. Azimifara, Z. Boostani, R. Shiraz Univ Sch Elect & Comp Engn Shiraz Iran Shiraz Univ Med Sci Med Management & Informat Sch Shiraz Iran

Video understanding is an important goal of several computer vision problems. To achieve this goal, a video is decomposed into a set of key components and the interactions between the components are modeled. Human action recognition is a challenging example of video understanding in the field of computer vision. Modeling a vocabulary of local image features in a bag of visual words (BoW) is a common approach to extract the components of an action video. Since in a video recognition task, there is no direct mapping from the raw features to class label, higher level visual descriptors and indeed, more accurate dictionaries are required. Therefore, in order to extract intrinsic shape bases and to consider temporal structure of an action, in this paper we take the advantages of group sparse coding methods. In our proposed BoW method each video is represented as a histogram of the coefficients obtained from group sparse coding. The main contribution of this study is to explore the geometry of action components via structured sparse coefficients of visual words in a real-time manner. In comparison with the conventional BoW models, our proposed approach has other advantages including much less quantization error, higher level feature representation which leads to reduction in model parameters and memory complexity while considering temporal structure. We evaluate our method on standard human action datasets including KTH, Weismann, UCF-sports and UCF50 human action datasets. The experimental results are significantly improved in comparison with previously presented results methods. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Human action recognition Unsupervised features learning Structured sparsity group sparse coding Linear SVM

来源：评论

学校读者我要写书评

暂无评论

Local image tagging via graph regularized joint group sparsity

引用

PATTERN RECOGNITION 2013年第5期46卷 1358-1368页

作者： Yang, Yang Huang, Zi Yang, Yi Liu, Jiajun Shen, Heng Tao Luo, Jiebo Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld 4072 Australia Carnegie Mellon Univ Sch Comp Sci Pittsburgh PA 15213 USA Univ Rochester Dept Comp Sci Rochester NY 14627 USA

In recent years, massive amounts of web image data have been emerging on the web. How to precisely label these images is critical and challenging to modem image search engines. Due to the fact that web image contents are more and more complex, existing image-level tagging methods may become less effective and hardly achieve satisfactory performance. This raises an urgent need for the fine-grained tagging, e.g., region-level tagging. In this work, we study how to establish mapping between tags and image regions. In particular, a novel hierarchical local image tagging method is proposed to simultaneously assign tags to all the regions within the same image. We propose a Laplacian Joint group Lasso (LJGL) model to jointly reconstruct the regions within a test image with a set of labeled training data. The LJGL model not only considers the robust encoding ability of joint group lasso but also preserves local structural information embedded in test regions. Besides, we extend the LJGL model to a kernel version in order to achieve the non-linear reconstruction. An effective algorithm is devised to optimize the objective function of the proposed model. Tags of training data are propagated to the reconstructed regions according to the reconstruction coefficients. Extensive experiments on four public image datasets demonstrate that our proposed models achieve significant performance improvements over the state-of-the-art methods in local image tagging. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Local image tagging group sparse coding Graph regularization Tag propagation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：