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›› 2010, Vol. 25 ›› Issue (4): 750-760.

• Special Section on Advances in Machine Learning and Applications •

### Multiple Hypergraph Clustering ofWeb Images by MiningWord2Image Correlations

Fei Wu(吴 飞), Senior Member, CCF, Ya-Hong Han(韩亚洪), and Yue-Ting Zhuang(庄越挺), Member, IEEE

1. College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
• Received:2009-05-15 Revised:2010-02-11 Online:2010-07-09 Published:2010-07-09
• About author:
Fei Wu is a senior member of CCF. He received the B.S. degree from Lanzhou University, China, the M.S. degree from Macao University, China, and the Ph.D. degree from Zhejiang University, Hangzhou, China. His research interest includes multimedia retrieval and statistic learning.
Ya-Hong Han received the B.S. degree in 2000 from Zhengzhou University, China, the M.S. degree in 2003 from Hohai University, Nanjing, China. He is currently pursuing the Ph.D. degree at the College of Computer Science, Zhejiang University. His research interests are machine learning and multimedia retrieval.
Yue-Ting Zhuang is a Member of IEEE. He received the B.S., M.S., and Ph.D. degrees from Zhejiang University, Hangzhou, China, in 1986, 1989, and 1998, respectively. Currently, he is a professor and Ph.D. supervisor at the College of Computer Science, Zhejiang University. His research interests include multimedia databases, artificial intelligence, and video-based animation.
• Supported by:

Supported by the National Natural Science Foundation of China under Grant Nos. 90920303, 60833006; the National Basic Research 973 Program of China under Grant No. 2010CB327905; the Program for Changjiang Scholars and Innovative Research Team in University of China under Grant Nos. IRT0652, PCSIRT.

In this paper, we consider the problem of clustering Web images by mining correlations between images and their corresponding words. Since Web images always come with associated text, the corresponding textual tags of Web images are used as a source to enhance the description of Web images. However, each word has different contribution for the interpretation of image semantics. Therefore, in order to evaluate the importance of each corresponding word of Web images, we propose a novel visibility model to compute the extent to which a word can be perceived visually in images, and then infer the correlation of word to image by the integration of visibility with tf-idf. Furthermore, Latent Dirichlet Allocation (LDA) is used to discover topic information inherent in surrounding text and topic correlations of images could be defined for image clustering. For integrating visibility and latent topic information into an image clustering framework, we first represent textual correlated and latent-topic correlated images by two hypergraph views, and then the proposed Spectral Multiple Hypergraph Clustering (SMHC) algorithm is used to cluster images into categories. The SMHC could be regarded as a new unsupervised learning process with two hypergraphs to classify Web images. Experimental results show that the SMHC algorithm has better clustering performance and the proposed SMHC-based image clustering framework is effective.

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