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

Jingrui He, Mingjing Li, Zhiwei Li, Hong Jiang Zhang, Hanghang Tong, Changshui Zhang

Research output: Contribution to journalArticle

8 Scopus citations

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.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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