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Title:
a novel duplicate images detection method based on plsa model
Author: Liao Xiaofeng ; Wang Yongji ; Ding Liping ; Gu Jian
Source: Proceedings of SPIE - The International Society for Optical Engineering
Conference Name: 4th International Conference on Machine Vision: Machine Vision, Image Processing, and Pattern Analysis, ICMV 2011
Conference Date: December 9, 2011 - December 10, 2011
Issued Date: 2012
Conference Place: Singapore, Singapore
Keyword: Affine transforms ; Clustering algorithms ; Image retrieval ; Semantics
Indexed Type: EI
ISSN: 0277-786X
ISBN: 9780819490254
Department: (1) Institute of Software Chinese Academy of Science Beijing 100190 China; (2) Graduate University of Chinese Academy of Sciences Beijing 100049 China; (3) Information Engineering School Nanchang University Nanchang Jiangxi 330031 China; (4) Key Lab. of Information Network Security of Ministry of Public Security Third Research Institute of Ministry of Public Security Shanghai 200031 China
Sponsorship: Int. Assoc. Comput. Sci. Inf. Technol. (IACSIT)
Abstract: Web image search results usually contain duplicate copies. This paper considers the problem of detecting and clustering duplicate images contained in web image search results. Detecting and clustering the duplicate images together facilitates users' viewing. A novel method is presented in this paper to detect and cluster duplicate images by measuring similarity between their topics. More specifically, images are viewed as documents consisting of visual words formed by vector quantizing the affine invariant visual features. Then a statistical model widely used in text domain, the PLSA(Probabilistic Latent Semantic Analysis) model, is utilized to map images into a probabilistic latent semantic space. Because the main content remains unchanged despite small digital alteration, duplicate images will be close to each other in the derived semantic space. Based on this, a simple clustering process can successfully detect duplicate images and cluster them together. Comparing to those methods based on comparison between hash value of visual words, this method is more robust to the visual feature level alteration posed on the images. Experiments demonstrates the effectiveness of this method. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).
English Abstract: Web image search results usually contain duplicate copies. This paper considers the problem of detecting and clustering duplicate images contained in web image search results. Detecting and clustering the duplicate images together facilitates users' viewing. A novel method is presented in this paper to detect and cluster duplicate images by measuring similarity between their topics. More specifically, images are viewed as documents consisting of visual words formed by vector quantizing the affine invariant visual features. Then a statistical model widely used in text domain, the PLSA(Probabilistic Latent Semantic Analysis) model, is utilized to map images into a probabilistic latent semantic space. Because the main content remains unchanged despite small digital alteration, duplicate images will be close to each other in the derived semantic space. Based on this, a simple clustering process can successfully detect duplicate images and cluster them together. Comparing to those methods based on comparison between hash value of visual words, this method is more robust to the visual feature level alteration posed on the images. Experiments demonstrates the effectiveness of this method. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/15725
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Liao Xiaofeng,Wang Yongji,Ding Liping,et al. a novel duplicate images detection method based on plsa model[C]. 见:4th International Conference on Machine Vision: Machine Vision, Image Processing, and Pattern Analysis, ICMV 2011. Singapore, Singapore. December 9, 2011 - December 10, 2011.
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