Speeding up active relevance feedback with approximate kNN retrieval for hyperplane queries

Michel Crucianu, Daniel Estevez, Vincent Oria, Jean Philippe Tarel

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

In content-based image retrieval, relevance feedback (RF) is a prominent method for reducing the "semantic gap" between the low-level features describing the content and the usually higher-level meaning of user's target. Recent RF methods are able to identify complex target classes after relatively few feedback iterations. However, because the computational complexity of such methods is linear in the size of the database, retrieval can be quite slow on very large databases. To address this scalability issue for active learning-based RF, we put forward a method that consists in the construction of an index in the feature space associated to a kernel function and in performing approximate kNN hyperplane queries with this feature space index. The experimental evaluation performed on two image databases show that a significant speedup can be achieved at the expense of a limited increase in the number of feedback rounds.

Original languageEnglish (US)
Pages (from-to)150-159
Number of pages10
JournalInternational Journal of Imaging Systems and Technology
Volume18
Issue number2-3
DOIs
StatePublished - 2008

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Software
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Keywords

  • Approximate search
  • Content-based image retrieval
  • Hyperplane query
  • M-tree
  • Relevance feedback
  • Scalability

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