TY - CHAP
T1 - A Discussion of Nonlinear Variants of Biased Discriminants for Interactive Image Retrieval
AU - Zhou, Xiang Scan
AU - Garg, Ashutosh
AU - Huang, Thomas S.
PY - 2004
Y1 - 2004
N2 - During an interactive image retrieval process with relevance feedback, kernel-based or boosted learning algorithms can provide superior nonlinear modeling capability. In this paper, we discuss such nonlinear extensions for biased discriminants, or BiasMap [1,2]. Kernel partial alignment is proposed as the criterion for kernel selection. The associated analysis also provides a gauge on relative class scatters, which can guide an asymmetric learner, such as BiasMap, toward better class modeling. We also propose two boosted versions of BiasMap. Unlike existing approach that boosts feature components or vectors to form a composite classifier, our scheme boosts linear BiasMap toward a nonlinear ranker which is more suited for small-sample learning during interactive image retrieval. Experiments on heterogeneous image database retrieval as well as small sample face retrieval are used for performance evaluations.
AB - During an interactive image retrieval process with relevance feedback, kernel-based or boosted learning algorithms can provide superior nonlinear modeling capability. In this paper, we discuss such nonlinear extensions for biased discriminants, or BiasMap [1,2]. Kernel partial alignment is proposed as the criterion for kernel selection. The associated analysis also provides a gauge on relative class scatters, which can guide an asymmetric learner, such as BiasMap, toward better class modeling. We also propose two boosted versions of BiasMap. Unlike existing approach that boosts feature components or vectors to form a composite classifier, our scheme boosts linear BiasMap toward a nonlinear ranker which is more suited for small-sample learning during interactive image retrieval. Experiments on heterogeneous image database retrieval as well as small sample face retrieval are used for performance evaluations.
UR - http://www.scopus.com/inward/record.url?scp=33845566516&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33845566516&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-27814-6_43
DO - 10.1007/978-3-540-27814-6_43
M3 - Chapter
AN - SCOPUS:33845566516
SN - 3540225390
SN - 9783540225393
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 353
EP - 364
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A2 - Enser, Peter
A2 - Kompatsiaris, Yiannis
A2 - O’Connor, Noel E.
A2 - Smeaton, Alan F.
A2 - Smeulders, Arnold W. M.
PB - Springer
ER -