Integrating local one-class classifiers for image retrieval

Authors:
Yiqing Tu;Gang Li;Honghua Dai
Affiliations:
School of Engineering and Information Technology, Deakin University, Vic, Australia;School of Engineering and Information Technology, Deakin University, Vic, Australia;School of Engineering and Information Technology, Deakin University, Vic, Australia
Venue:
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Year:
2006

Citing 4
Cited 0

Bagging predictors

Machine Learning
Performance evaluation in content-based image retrieval: overview and proposals

Pattern Recognition Letters - Special issue on image/video indexing and retrieval
Negative pseudo-relevance feedback in content-based video retrieval

MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
A needle in a haystack: local one-class optimization

ICML '04 Proceedings of the twenty-first international conference on Machine learning

Quantified Score

Hi-index	0.00

Visualization

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.