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题名:
Large Sparse Cone Non-negative Matrix Factorization for Image Annotation
作者: Tao, Dapeng1; Tao, Dacheng2,3; Li, Xuelong4; Gao, Xinbo5
作者部门: 光学影像学习与分析中心
刊名: ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
出版日期: 2017-04-01
卷号: 8, 期号:3
关键词: Non-negative matrix factorization ; image annotation ; Nesterovs optimal gradient ; sparseness constraint
DOI: 10.1145/2987379
文章类型: Article
英文摘要:

Image annotation assigns relevant tags to query images based on their semantic contents. Since Non-negative Matrix Factorization (NMF) has the strong ability to learn parts-based representations, recently, a number of algorithms based on NMF have been proposed for image annotation and have achieved good performance. However, most of the efforts have focused on the representations of images and annotations. The properties of the semantic parts have not been well studied. In this article, we revisit the sparseness-constrained NMF (sNMF) proposed by Hoyer [ 2004]. By endowing the sparseness constraint with a geometric interpretation and sNMF with theoretical analyses of the generalization ability, we show that NMF with such a sparseness constraint has three advantages for image annotation tasks: (i) The sparseness constraint is more l(0)-norm oriented than the l(0)-norm-based sparseness, which significantly enhances the ability of NMF to robustly learn semantic parts. (ii) The sparseness constraint has a large cone interpretation and thus allows the reconstruction error of NMF to be smaller, which means that the learned semantic parts are more powerful to represent images for tagging. (iii) The learned semantic parts are less correlated, which increases the discriminative ability for annotating images. Moreover, we present a new efficient large sparse cone NMF (LsCNMF) algorithm to optimize the sNMF problem by employing the Nesterov's optimal gradient method. We conducted experiments on the PASCAL VOC07 dataset and demonstrated the effectiveness of LsCNMF for image annotation.

WOS标题词: Science & Technology ; Technology
类目[WOS]: Computer Science, Artificial Intelligence ; Computer Science, Information Systems
研究领域[WOS]: Computer Science
关键词[WOS]: SOCIAL MULTIMEDIA ; RECOGNITION ; NETWORKS ; WAVELET
收录类别: SCI ; EI
项目资助者: National Natural Science Foundation of China(61572486 ; Yunnan Natural Science Funds(2016FB105) ; Guangdong Natural Science Funds(2014A030310252) ; Shenzhen Technology Project(JCYJ20140901003939001) ; Opening Project of State Key Laboratory of Digital Publishing Technology ; Program for Excellent Young Talents of Yunnan University ; Australian Research Council(FT-130101457 ; Program for Changjiang Scholars and Innovative Research Team in University of China(IRT13088) ; 61432014) ; DP-140102164 ; LE-140100061)
语种: 英语
WOS记录号: WOS:000400160800004
ISSN号: 2157-6904
产权排序: 3
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.opt.ac.cn/handle/181661/28864
Appears in Collections:光学影像学习与分析中心_期刊论文

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作者单位: 1.Yunnan Univ, Sch Informat Sci & Engn, Kunming 650091, Peoples R China
2.Univ Sydney, Sch Informat Technol, J12-318 Cleveland St, Darlington, NSW 2008, Australia
3.Univ Sydney, Fac Engn & Informat Technol, J12-318 Cleveland St, Darlington, NSW 2008, Australia
4.Chinese Acad Sci, Ctr OPT IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China
5.Xidian Univ, State Key Lab Integrated Serv Networks, Sch Elect Engn, Xian 710071, Peoples R China

Recommended Citation:
Tao, Dapeng,Tao, Dacheng,Li, Xuelong,et al. Large Sparse Cone Non-negative Matrix Factorization for Image Annotation[J]. ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,2017,8(3).
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