In region-based image retrieval, not all the regions are important for retrieving similar images and rather, the user is often interested in performing a query on only salient regions. Therefore, we propose a new method for extraction of salient regions using Support Vector Machines (SVM) and a method for importance score learning according to the user's interaction. Once an image is segmented, our algorithm permits the Attention Window (AW) according to the variation of an image and selects salient regions by using the pre-defined feature vector and SVM within the AW. By using SVM, we do not need to determine the heuristic feature parameters and produce more reasonable results. The distance values from SVM are used for initial importance scores of salient regions and our proposed updating algorithm using relevance feedback updates them automatically. Through performance comparison with parametric salient extraction method, our proposed method shows better performance as well as semantic query interface for object-level image retrieval.
|Number of pages||4|
|Journal||Proceedings - International Conference on Pattern Recognition|
|Publication status||Published - 2004 Dec 17|
|Event||Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004 - Cambridge, United Kingdom|
Duration: 2004 Aug 23 → 2004 Aug 26
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition