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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Tu, Y., Li, G., Dai, H. (2006). Integrating Local One-Class Classifiers for Image Retrieval. In: Li, X., ZaĂŻane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_24
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DOI: https://doi.org/10.1007/11811305_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
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