Integrating Local One-Class Classifiers for Image Retrieval
In content-based image retrieval, learning from users’ feedback can be considered as an one-class classification problem. However, the OCIB method proposed in  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.
KeywordsImage Retrieval Decision Function Mean Average Precision Ranking List Relevant Image
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