Efficient relevance feedback for content-based image retrieval by mining user navigation patterns

Ja Hwung Su*, Wei Jyun Huang, Philip S. Yu, S. Tseng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

133 Scopus citations

Abstract

Nowadays, content-based image retrieval (CBIR) is the mainstay of image retrieval systems. To be more profitable, relevance feedback techniques were incorporated into CBIR such that more precise results can be obtained by taking user's feedbacks into account. However, existing relevance feedback-based CBIR methods usually request a number of iterative feedbacks to produce refined search results, especially in a large-scale image database. This is impractical and inefficient in real applications. In this paper, we propose a novel method, Navigation-Pattern-based Relevance Feedback (NPRF), to achieve the high efficiency and effectiveness of CBIR in coping with the large-scale image data. In terms of efficiency, the iterations of feedback are reduced substantially by using the navigation patterns discovered from the user query log. In terms of effectiveness, our proposed search algorithm NPRFSearch makes use of the discovered navigation patterns and three kinds of query refinement strategies, Query Point Movement (QPM), Query Reweighting (QR), and Query Expansion (QEX), to converge the search space toward the user's intention effectively. By using NPRF method, high quality of image retrieval on RF can be achieved in a small number of feedbacks. The experimental results reveal that NPRF outperforms other existing methods significantly in terms of precision, coverage, and number of feedbacks.

Original languageEnglish
Article number5539759
Pages (from-to)360-372
Number of pages13
JournalIEEE Transactions on Knowledge and Data Engineering
Volume23
Issue number3
DOIs
StatePublished - 31 Jan 2011

Keywords

  • Content-based image retrieval
  • navigation pattern mining
  • query expansion
  • query point movement
  • relevance feedback

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