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Integrating Local One-Class Classifiers for Image Retrieval.
- Source :
- Advanced Data Mining & Applications (9783540370253); 2006, p213-222, 10p
- Publication Year :
- 2006
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540370253
- Database :
- Complementary Index
- Journal :
- Advanced Data Mining & Applications (9783540370253)
- Publication Type :
- Book
- Accession number :
- 32864272
- Full Text :
- https://doi.org/10.1007/11811305_24