Bayesian Multi-instance Learning for Image Retrieval with Unlabeled Data

DOI

10.2991/iccsee.2013.425How to use a DOI?

Keywords

content-based image retrieval, Bayesian classifier, multi-instance learning, machine learning

Abstract

To deal with the two problems in image retrieval, i.e., the small number of query images, the ambiguity of an image - the image consists of many regions with different semantic meaning, in this paper, we proposed a novel method for image retrieval based on Bayesian multi-instance learning using unlabeled data, termed as Bayesian-MIL method, which treats the image retrieval as a binary classification problem. In this method, to obtain an approximate estimation of the class-conditional probability of positive images, a multi-instance learning algorithm is adopted to filter out background regions in positive images, and then a Bayesian classifier is constructed to rank the images from a large digital repository according to their score of posterior probability. Finally, the ranking top k images will be returned to users. Experimental results on COREL image data set have demonstrated the effectiveness and efficiency of the proposed approach.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Tao CHEN
AU  - Huifang DENG
PY  - 2013/03
DA  - 2013/03
TI  - Bayesian Multi-instance Learning for Image Retrieval with Unlabeled Data
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 1698
EP  - 1702
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.425
DO  - 10.2991/iccsee.2013.425
ID  - CHEN2013/03
ER  -