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Integrating local one-class classifiers for image retrieval

Tu, Y., Li, Gang and Dai, Honghua 2006, Integrating local one-class classifiers for image retrieval, Lecture notes in computer science, vol. 4093, pp. 213-222, doi: 10.1007/11811305_24.

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Title Integrating local one-class classifiers for image retrieval
Author(s) Tu, Y.
Li, GangORCID iD for Li, Gang
Dai, Honghua
Journal name Lecture notes in computer science
Volume number 4093
Start page 213
End page 222
Publisher Springer Berlin
Place of publication Berlin, Germany
Publication date 2006
ISSN 0302-9743
Summary In content-based image retrieval, learning from users’ feedback can be considered as an one-class classification problem. However, the OCIB method proposed in [1] suffers from the problem that it is only a one-mode method which cannot deal with multiple interest regions. In addition, it requires a pre-specified radius which is usually unavailable in real world applications. This paper overcomes these two problems by introducing ensemble learning into the OCIB method: by Bagging, we can construct a group of one-class classifiers which emphasize various parts of the data set; this is followed by a rank aggregating with which results from different parameter settings are incorporated into a single final ranking list. The experimental results show that the proposed I-OCIB method outperforms the OCIB for image retrieval applications.

Notes SpringerLink Date Thursday, July 27, 2006
Language eng
DOI 10.1007/11811305_24
Field of Research 080299 Computation Theory and Mathematics not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2006, Springer-Verlag Berlin Heidelberg
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Document type: Journal Article
Collection: School of Engineering and Information Technology
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