Relevance feedback using adaptive clustering for region based image similarity retrieval

Authors:
Deok-Hwan Kim;Seok-Lyong Lee
Affiliations:
School of Electronics and Electrical Engineering, Inha University, Incheon, Korea;School of Industrial and Information Systems Engineering, Hankuk University of Foreign Studies, Yongin-si, Kyounggi-do, Korea
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

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Abstract

In this paper, we propose a novel relevance feedback approach using adaptive clustering based on region representation. Performance of content based image retrieval system is usually very low because of the semantic gap between the low level feature representation and the user's high level concept in a query image. Semantically relevant images may exhibit very different visual characteristics, and may be scattered in several clusters. Our main goal is finding semantically related clusters to reduce this semantic gap. Our method consists of region based clustering process and cluster-merging process. All segmented regions of relevant images are grouped into semantically related clusters, and clusters are merged by estimating the number of the clusters. We form representatives of clusters as the optimal query. A region based image similarity measure is used to calculate the distance between the multipoint optimal query and an image in the database. Experiments have demonstrated that the proposed approach is effective in improving the performance of image similarity retrieval system.