检索结果-内蒙古大学图书馆

Multi-Order Neighborhood Fusion Based Multi-View Deep Subspace Clustering

Computers, Materials & Continua 2025年第3期82卷 3873-3890页

作者： Kai Zhou Yanan Bai Yongli Hu Boyue Wang Department of Automation Tsinghua UniversityBeijing100084China National Center of Technology Innovation for Intelligentization of Politics and Law Beijing100000China Beijing Key Lab of Intelligent Telecommunication Software and Multimedia Beijing University of TechnologyBeijing100124China

Existing multi-view deep subspace clustering methods aim to learn a unified representation from multi-view data,while the learned representation is difficult to maintain the underlying structure hidden in the origin samples,especially the high-order neighbor relationship between *** overcome the above challenges,this paper proposes a novel multi-order neighborhood fusion based multi-view deep subspace clustering *** creatively integrate the multi-order proximity graph structures of different views into the self-expressive layer by a multi-order neighborhood fusion *** this design,the multi-order Laplacian matrix supervises the learning of the view-consistent self-representation affinity matrix;then,we can obtain an optimal global affinity matrix where each connected node belongs to one *** addition,the discriminative constraint between views is designed to further improve the clustering performance.A range of experiments on six public datasets demonstrates that the method performs better than other advanced multi-view clustering *** code is availab.e at https://***/songzuolong/MNF-MDSC(accessed on 25 December 2024).

关键词： Multi-view subspace clustering subspace clustering deep clustering multi-order graph structure

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Improved adaptive principal component extraction algorithm based on face recognition with morphable model

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Journal of Computational Information Systems 2005年第2期1卷 329-336页

作者： Yin, Baocai Shi, Qin Wang, Chengzhang Sun, Yanfeng Beijing Municipal Multimedia and Intelligent Software Key Lab. Science Beijing University of Technology Beijing 100022 China

In this present study, a face recognition method is proposed based on improved adaptive principal component extraction algorithm with Morphablc model. Improved adaptive principal component extraction algorithm is proposed to compress high-dimensional facial image data. A 3D Morphable model is adopted to derive multiple images of a face from a single facial image. The experimental results on ORL and UMIST face database show the proposed method gives impressive performance and improvement compared with the conventional Eigenface methods.

关键词： Face recognition

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Image super-resolution reconstruction based on sparse representation and low-rank matrix completion

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Journal of Information and Computational Science 2012年第13期9卷 3859-3866页

作者： Jing, Guodong Shi, Yunhui Yin, Baocai Beijing Municipal Key Lab. of Multimedia and Intelligent Software Technology College of Computer Science and Technology Beijing University of Technology Beijing 100124 China

This paper addresses the problem of generating a High-resolution (HR) image from a single Low-resolution (LR) image. We propose the super-resolution reconstruction approach based on sparse representation and low-rank matrix completion. The approach represents images in forms of sparse and rearranges image regions into the low dimension construction{matrices of low rank. High-frequency details of image are restored using the sparse representation which is recovered from the down-sampled images. For paths at the same position of multiple pictures which are obtained by several super resolution reconstructions are highly correlated, they are arranged to be a matrix of low-rank which can be completed exactly from corrupted entries. Experiment results demonstrate that the proposed method significantly improves the PSNR and visual quality of reconstructed high-resolution images. 1548-7741/Copyright © 2012 Binary Information Press.

关键词： Image enhancement

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A novel hybrid apporach based on normalized color spaces and 2DPCA for color face recognition

A novel hybrid apporach based on normalized color spaces and...

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2010 3rd International Congress on Image and Signal Processing, CISP 2010

作者： Chen, Shangyou Sun, Yanfeng Yin, Baocai Song, Caifang Beijing Municipal Key Lab. of Multimedia and Intelligent Software Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781424465149

Recently, in a task of face recognition, some researchers proposed a novel method called color space normalization (CSN) which can enhance the discriminating power of color spaces for color face recognition. This method can significantly reduce the correlation between the three color components RGB and thus can enhance the discriminating power of the concatenated color component RGB. This offers an intrinsic (or internal) reason for why the CSN techniques can improve the face recognition performance. The three color component images of normalized color spaces are concatenated into one pattern vector, then PCA+FLD is performed on the concatenated pattern vector. But the concatenated pattern vector based on PCA destroyed the overall structure of each pixel in image. Some local structure information may be ignored. While 2DPCA preserves the overall structure information well and eliminates the small sample problem on a certain extent. Therefore, in this paper, color space normalization was combined with 2DPCA for color face recognition. This CSN based 2DPCA was performed and compared with PCA, 2DPCA for color face recognition. Numerous experiments show that the proposed approach has better recognition performance than other traditional PCA-based methods for color face recognition. ©2010 IEEE.

关键词： Color

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Cross-media Hot Topic Auto-tracking Model Based on Semantics and Temporal Context

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Chinese Journal of Electronics 2015年第3期24卷 529-534页

作者： LIANG Meiyu DU Junping ZHOU Yipeng Beijing Key Lab of Intelligent Telecommunication Software and Multimedia School of Computer ScienceBeijing University of Posts and Telecommunications School of Computer and Information Engineering Beijing Technology and Business University

As the Internet multimedia information grows explosively, seeking an automatic technology to realize the effective organization and management of crossmedia emergency information is significantly necessary. A novel cross-media hot topic auto-tracking model based on semantics and temporal context is proposed in this paper. According to the semantic correlations of cross-media information, we learn the image visual semantics by the text semantics based on the Latent Dirichlet Allocation probability model, and establish the unified cross-media information description on the same semantic level. Also a semantics-based two-step feature dimension-reduction scheme is proposed to establish the efficient semantic feature space. The self-adaptive learning of topic model is realized to track the dynamic changes in the topic. Experimental results demonstrate that the proposed method outperforms the existing methods, which further improves the effect of hot topic auto-tracking.

关键词： Cross-media data mining Topic autotracking Semantic model Latent dirichlet allocation(LDA) Temporal context

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Feedback-based error tracking for AVS-M

Feedback-based error tracking for AVS-M

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2009 2nd International Congress on Image and Signal Processing, CISP'09

作者： Conghui, Shi Yunhui, Shi Baocai, Yin Beijing Key Lab. of Multimedia and Intelligent Software Technology Beijing University of Technology Beijing 100124 China

ISBN: (纸本)9781424441310

AVS-M is a video encoding standard used for mobile video in wireless environment, developed by the Audio and Video Coding Standard Working Group of China. In order to cope with the burst error in error-prone wireless networks, in this paper, we present an error tracking mechanism, which utilizing a feedback channel to judge which areas are contaminated by the prediction from its preceding frames. Through this positioning, we can terminate the error propagation effects by INTRA refreshing the affected areas. Simulations demonstrate that the proposed algorithm can effectively stop the quality degradation and not cause much bit-rate increase. ©2009 IEEE.

关键词： Video signal processing

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Color face recognition based on color space normalization and quaternion matrix representation

Color face recognition based on color space normalization an...

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4th International Conference on Digital Home, ICDH 2012

作者： Jia, Huajie Wang, Lichun Sun, Yanfeng Hu, Yongli Beijing Municipal Key Lab. of Multimedia and Intelligent Software Technology Beijing University of Technology Beijing China

The color face recognition method using quaternion matrix representation is based on RGB color space which is a fundamental and widely used space. However, the RGB color space is a relatively weak color space for face recognition due to the strong correlation between the three color channels. A technique named color space normalization (CSN) technique can reduce the correlation between the three channels of the RGB color space, and enhance the discriminating power of the normalized color space RGB-N. Therefore, in this paper, a method based on normalized color space RGB-N and quaternion matrix representation is firstly proposed. The proposed method not only has the advantage of quaternion matrix representation model, but also has the strong discriminating power due to CSN. The experiments in this paper are performed on the FERET color face database. Compared with the method based on RGB color space and quaternion matrix representation, the proposed approach in this paper can enhance the recognition rate. © 2012 IEEE.

关键词： Face recognition

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Local gabor binary pattern random subspace method for eyeglasses-face recognition

Local gabor binary pattern random subspace method for eyegla...

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2010 3rd International Congress on Image and Signal Processing, CISP 2010

作者： Liu, Li Sun, Yanfeng Yin, Baocai Song, Caifang Beijing Key Lab. of Multimedia and Intelligent Software Technology College of Computer Science and Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781424465149

In this paper, we introduce a novel Local Gabor Binary Pattern Random Subspace Method (LGBPRSM) for wearing-glasses face recognition. It extracts the discriminating features from facial space based on local-feature method, after that, it constructs multiple classifiers by randomly sampling from the feature set to gain more diversity between classifiers for efficiently recognizing the faces with glasses. Our experimental results on FERET and Yale database prove the advantages of the proposed approach when compared with other methods. ©2010 IEEE.

关键词： Face recognition

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Image super-resolution based on multi-space sparse representation 10

Image super-resolution based on multi-space sparse represent...

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Proceedings of the Second International Conference on Internet multimedia Computing and Service

作者： Jing, Guodong Shi, Yunhui Kong, Dehui Ding, Wenpeng Yin, Baocai Beijing Municipal Key Lab. of Multimedia and Intelligent Software Technology College of Computer Science and Technology Beijing University of Technology Beijing 100124 China

ISBN: (纸本)9781450304603

Sparse representation provides a new method of generating a super-resolution image from a single low resolution input image. An over-complete base for sparse representation is an essential part of such methods. However discovering the over-complete base with efficient representation from a large amount of image patches is a difficult problem. We make efforts in sparse representation and its implementation to solve the problem. In the representation, image patches are decomposed into two structure and texture components represented by the over-complete bases of their own spaces so that their high-level features can be captured by the bases. In the implementation, a prior knowledge about low resolution images generation is combined to the typical base construction for high construction quality. Finally a super-resolution construction based on multi-space sparse representation is proposed. Experiment results demonstrate that the proposed method significantly improve the PSNR and visual quality of reconstructed high-resolution image. Copyright 2010 ACM.

关键词： Image enhancement

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A Q-learning-based decision making scheme for application reconfiguration in sensor networks

A Q-learning-based decision making scheme for application re...

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11th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2007

作者： Zhang, Dongmei Ma, Huadong Beijing Key Lab. of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)1424409632

Application reconfiguration is a principal method of improving the application flexibility and adaptability to dynamic environment in sensor networks. Motivated by this, we propose a novel Q-learning-based decision making scheme in sensor networks to support the environment adaptive reconfiguration decision. In this scheme, we introduce the rule-based reasoning to predict the trend of environmental change and make reconfiguration decision adaptively. In particular, we apply a real-time reinforcement learning algorithm, called Q-learning, to evaluate the certainty value of the rules in this decision mechanism. Moreover, we map the continuous state space of environment condition into a discrete and finite state space to design a certainty function. The simulation results show that the Q-learning-based decision mechanism can not only learn the change of environment, but also effectively make the reconfiguration decision in sensor networks. ©2007 IEEE.

关键词： Decision making

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