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Relevance feedback in content-based image retrieval: some recent advances
- Source :
-
Information Sciences . Dec2002, Vol. 148 Issue 1-4, p129. 9p. - Publication Year :
- 2002
-
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. [Copyright &y& Elsevier]
- Subjects :
- *DISCRIMINANT analysis
*IMAGE retrieval
Subjects
Details
- Language :
- English
- ISSN :
- 00200255
- Volume :
- 148
- Issue :
- 1-4
- Database :
- Academic Search Index
- Journal :
- Information Sciences
- Publication Type :
- Periodical
- Accession number :
- 7788923
- Full Text :
- https://doi.org/10.1016/S0020-0255(02)00286-4