Relevance feedback using adaptive clustering for image similarity retrieval

  • Authors:

  • Deok-Hwan Kim;Chin-Wan Chung;Kobus Barnard

  • Affiliations:

  • Department of Mobile Internet, Dongyang Technical College, 62-160 Kochuk-dong, Kuro-gu, Seoul 152-714, South Korea;Division of Computer Science, Korea Advanced Institute of Science and Technology, 373-1, Kusong-dong, Yusong-gu, Taejon 305-701, South Korea;Computer Science Department, University of Arizona, Gould-Simpson 730, Tucson, AZ 85721, USA

  • Venue:
  • Journal of Systems and Software
  • Year:
  • 2005

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Abstract

Research has been devoted in recent years to relevance feedback as an effective solution to improve performance of image similarity search. However, few methods using the relevance feedback are currently available to perform relatively complex queries on large image databases. In the case of complex image queries, images with relevant concepts are often scattered across several visual regions in the feature space. This leads to adapting multiple regions to represent a query in the feature space. Therefore, it is necessary to handle disjunctive queries in the feature space. In this paper, we propose a new adaptive classification and cluster-merging method to find multiple regions and their arbitrary shapes of a complex image query. Our method achieves the same high retrieval quality regardless of the shapes of query regions since the measures used in our method are invariant under linear transformations. Extensive experiments show that the result of our method converges to the user's true information need fast, and the retrieval quality of our method is about 22% in recall and 20% in precision better than that of the query expansion approach, and about 35% in recall and about 31% in precision better than that of the query point movement approach, in MARS.