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Proceedings Paper

A novel duplicate images detection method based on PLSA model
Author(s): Xiaofeng Liao; Yongji Wang; Liping Ding; Jian Gu
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Paper 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.

Paper Details

Date Published: 11 January 2012
PDF: 8 pages
Proc. SPIE 8349, Fourth International Conference on Machine Vision (ICMV 2011): Machine Vision, Image Processing, and Pattern Analysis, 834909 (11 January 2012); doi: 10.1117/12.920153
Show Author Affiliations
Xiaofeng Liao, Institute of Software (China)
Graduate Univ. of the Chinese Academy of Sciences (China)
Nanchang Univ. (China)
Yongji Wang, Institute of Software (China)
Liping Ding, Institute of Software (China)
Jian Gu, Ministry of Public Security (China)

Published in SPIE Proceedings Vol. 8349:
Fourth International Conference on Machine Vision (ICMV 2011): Machine Vision, Image Processing, and Pattern Analysis
Zhu Zeng; Yuting Li, Editor(s)

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