Relevance feedback in content-based image retrieval: Some recent advances
Inhaltsverzeichnis
Referenz
X. S. Zhou and T. S. Huang: Relevance feedback in content-based image retrieval: Some recent advances. Inf. Sci., vol. 148, no. 1–4, pp. 129-137, 2002
DOI
http://dx.doi.org/10.1016/S0020-0255(02)00286-4
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 word association via relevance feedback (WARF) formula is presented and tested for unification of low-level visual features and high-level semantic annotations during the process of relevance feedback.
Extended Abstract
Bibtex
@article{Zhou2002129,
title = "Relevance feedback in content-based image retrieval: some recent advances ",
journal = "Information Sciences ",
volume = "148",
number = "1–4",
pages = "129 - 137",
year = "2002",
note = "",
issn = "0020-0255",
doi = "http://dx.doi.org/10.1016/S0020-0255(02)00286-4",
url = "http://www.sciencedirect.com/science/article/pii/S0020025502002864 http://de.evo-art.org/index.php?title=Relevance_feedback_in_content-based_image_retrieval:_Some_recent_advances",
author = "Xiang Sean Zhou and Thomas S Huang"
}
Used References
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