Extracting salient regions and learning importance scores in region-based image retrieval

Byoungchul Ko, Hyeran Byun

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

In this paper, we propose a new method for extracting salient regions and learning their importance scores in region-based image retrieval. In Region-Based Image Retrieval (RBIR), not all the regions are important for retrieving similar images and rather, in retrieval, the user is often interested in performing a query on only one or a few regions rather than the whole image. Therefore, for a successful retrieval system, it is an important issue to specify which regions are important for retrieving an image. To extract salient regions from images automatically, we make three assumptions and determine salient regions with their importance scores. In this paper, we apply the relevance feedback algorithm to the matching process as two different purposes: one is for updating importance scores of salient regions and the other is for updating weights of feature vectors. By using our relevance feedback method, the matching process can improve retrieval performance interactively and allow progressive refinement of query results according to the user's feedback action. Through experiments and comparison with other methods, our proposed method shows good performance as well as easy and semantic interface for region-based image retrieval. The efficacy of our method is validated using a set of 3000 images from Corel-photo CD.

Original languageEnglish
Pages (from-to)1349-1367
Number of pages19
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume17
Issue number8
DOIs
Publication statusPublished - 2003 Dec

Bibliographical note

Funding Information:
This work was supported by Biometrics Engineering Research Center (KOSEF).

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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