Multiclass VisualRank: image ranking method in clustered subsets based on visual features

Authors:
Mitsuru Ambai;Yuichi Yoshida
Affiliations:
Denso IT Laboratory, Inc., Shibuya Shibuya-ku Tokyo, Japan;Denso IT Laboratory, Inc., Shibuya Shibuya-ku Tokyo, Japan
Venue:
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Year:
2009

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Abstract

This paper proposes Multiclass VisualRank, a method that expands the idea of VisualRank into more than one category of images. Multiclass VisualRank divides images retrieved from search engines into several categories based on distinctive patterns of visual features, and gives ranking within the category. Experimental results show that our method can extract several different image categories relevant to given keyword and gives good ranking scores to retrieved images.