@inproceedings{7b30c44032a14fc1b6bfe6c7b51b63ce,
title = "Relevance Feedback in Content-Based Image Retrieval: Some Recent Advances",
abstract = "Various relevance feedback algorithms have been proposed in recent years in the area of content-based image retrieval. This paper presents some recent advances: first, the linear and kernel-based biased discriminant analysis, BiasMap, is proposed to fit the unique nature of relevance feedback as a small sample biased classification problem. As a novel variant of traditional discriminant analysis, the proposed algorithm provides a trade-off between discriminant transform and density modeling. Experimental results indicate that significant improvement in retrieval performance is achieved by the new scheme. Secondly, a WARF (word association via relevance feedback) formula is presented and tested for unification of low-level visual features and high-level semantic annotations during the process of relevance feedback.",
author = "Zhou, {Xiang Sean} and Huang, {Thomas S.}",
note = "Funding Information: This work was supported in part by NSF Grant CDA 96-24396 and EIA 99-75019. ; Proceedings of the 6th Joint Conference on Information Sciences, JCIS 2002 ; Conference date: 08-03-2002 Through 13-03-2002",
year = "2002",
language = "English (US)",
isbn = "0970789017",
series = "Proceedings of the Joint Conference on Information Sciences",
pages = "15--18",
editor = "J.H. Caulfield and S.H. Chen and H.D. Cheng and R. Duro and J.H. Caufield and S.H. Chen and H.D. Cheng and R. Duro and V. Honavar",
booktitle = "Proceedings of the 6th Joint Conference on Information Sciences, JCIS 2002",
}