Semantic clustering for region-based image retrieval

Authors:
Ying Liu;Xin Chen;Chengcui Zhang;Alan Sprague
Affiliations:
Department of Computer and Information Science, University of Alabama at Birmingham, CH 127, 1530 3rd Avenue S., Birmingham, AL 35294, USA;Department of Computer and Information Science, University of Alabama at Birmingham, CH 127, 1530 3rd Avenue S., Birmingham, AL 35294, USA;Department of Computer and Information Science, University of Alabama at Birmingham, CH 127, 1530 3rd Avenue S., Birmingham, AL 35294, USA;Department of Computer and Information Science, University of Alabama at Birmingham, CH 127, 1530 3rd Avenue S., Birmingham, AL 35294, USA
Venue:
Journal of Visual Communication and Image Representation
Year:
2009

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Abstract

With the proliferation of applications that demand content-based image retrieval, two merits are becoming more desirable. The first is the reduced search space, and the second is the reduced ''semantic gap.'' This paper proposes a semantic clustering scheme to achieve these two goals. By performing clustering before image retrieval, the search space can be significantly reduced. The proposed method is different from existing image clustering methods as follows: (1) it is region based, meaning that image sub-regions, instead of the whole image, are grouped into. The semantic similarities among image regions are collected over the user query and feedback history; (2) the clustering scheme is dynamic in the sense that it can evolve to include more new semantic categories. Ideally, one cluster approximates one semantic concept or a small set of closely related semantic concepts, based on which the ''semantic gap'' in the retrieval is reduced.