Biased isomap projections for interactive reranking
- Publication Type:
- Conference Proceeding
- Proceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009, 2009, pp. 1632 - 1635
- Issue Date:
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Image search has recently gained more and more attention for various applications. To capture users' intensions and to bridge the gap between the low level visual features and the high level semantics, a dozen of interactive reranking (IR) or relevance feedback (RF) algorithms have been developed and achieved significant performance improvements. In this paper, we develop a novel subspace learning based IR algorithm by using the patch alignment framework, termed the biased ISOMap projections or BIP for short. BIP models both the intraclass local geometry for query relevant images and the interclass discrimination between query relevant images and irrelevant images. In addition, BIP never meets the small samples size problem. We present experimental evidence suggesting that BIP is effective for targeting the intensions of users and reducing the semantic gaps for image search. ©2009 IEEE.
Please use this identifier to cite or link to this item: