Relevance feedback using adaptive clustering for image similarity retrieval

Deok Hwan Kim, Chin Wan Chung, Kobus Barnard

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)9-23
Number of pages15
JournalJournal of Systems and Software
Issue number1
StatePublished - Oct 2005


  • Classification
  • Cluster-merging
  • Content-based image retrieval
  • Dimension reduction
  • Image database
  • Relevance feedback

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture


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