Pseudo relevance feedback based on iterative probabilistic one-class SVMs in web image retrieval

Authors:
Jingrui He;Mingjing Li;Zhiwei Li;Hong-Jiang Zhang;Hanghang Tong;Changshui Zhang
Affiliations:
Automation Department, Tsinghua University, Beijing, P.R.China;Microsoft Research Asia, Beijing, P.R.China;Microsoft Research Asia, Beijing, P.R.China;Microsoft Research Asia, Beijing, P.R.China;Automation Department, Tsinghua University, Beijing, P.R.China;Automation Department, Tsinghua University, Beijing, P.R.China
Venue:
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
Year:
2004

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Abstract

To improve the precision of top-ranked images returned by a web image search engine, we propose in this paper a novel pseudo relevance feedback method named iterative probabilistic one-class SVMs to re-rank the retrieved images. By assuming that most top-ranked images are relevant to the query, we iteratively train one-class SVMs, and convert the outputs to probabilities so as to combine the decision from different image representation. The effectiveness of our method is validated by systematic experiments even if the assumption is not well satisfied.