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.
|Number of pages
|International Journal of Pattern Recognition and Artificial Intelligence
|Published - 2003 Dec
Bibliographical noteFunding Information:
The authors gratefully acknowledge the research assistance of Tina Rogers in producing this article, the reviews of Jean-Louis d'Auzon and Philippe Bouchet, and the assistance of Aaron Bauer, Jean Chazeau, Gavin Hunt and Bernard Seret. Support for this work was provided by Conservation International's Melanesia Program and the John D. and Catherine T. MacArthur Foundation's World Environment and Resources Program.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Artificial Intelligence