Biased ISOMap projections for interactive reranking

Authors:
Wei Bian;Jun Cheng;Dacheng Tao
Affiliations:
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences and School of Computer Engineering, Nanyang Technological University, Singapore;Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences;School of Computer Engineering, Nanyang Technological University, Singapore
Venue:
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Year:
2009

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Abstract

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.