A novel method for image retrieval using relevance feedback and unsupervised clustering

  • Authors:
  • S. M. Renuka Devi;Chakravarthy Bhagvati

  • Affiliations:
  • G. Narayanamma Institute of Technology & Science, Hyderabad;University of Hyderabad

  • Venue:
  • COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

The standard approach to content-based image retrieval is currently concerned with bridging the semantic gap or the gap between the results produced by the use of low-level features and the human end-user expectations based on high-level semantics. In this paper, we suggest that there are advantages to bridging the gap in two stages by proposing an intermediate level. We show that unsupervised clustering of low-level image features provides a suitable basis for an intermediate level representation and define a CBIR system using such an approach. The main advantages of using an intermediate level are (a) it is not necessary for all positive responses to a user query be categorized into a single class; (b) it is possible to overcome the small-sample problem with too few positive examples; and, (c) to improve performance without greatly increased computational cost. Experimental results on Wang's database (1000 images) and Corel Photo gallery (10,800 images) show that the intermediate level analysis leads to better results.