Enhancing content-based image retrieval using machine learning techniques

Authors:
Qinmin Vivian Hu;Zheng Ye;Xiangji Jimmy Huang
Affiliations:
Information Retrieval and Knowledge Management Research Lab, York University, Toronto, Canada;Information Retrieval and Knowledge Management Research Lab, York University, Toronto, Canada and Information Retrieval Lab, Dalian University of Technology, Dalian, China;Information Retrieval and Knowledge Management Research Lab, York University, Toronto, Canada
Venue:
AMT'10 Proceedings of the 6th international conference on Active media technology
Year:
2010

Citing 14
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Abstract

In this paper, we propose a term selection model to help select terms in the documents that describe the images to improve the content-based image retrieval performance. First, we introduce a general feature selection model. Second, we present a painless way for training document collections, followed by selecting and ranking the terms using the Kullback-Leibler Divergence. After that, we learn the terms by the classification method, and test it on the content-based image retrieval result. Finally, we setup a series of experiments to confirm that the model is promising. Furthermore, we suggest the optimal values for the number maxK and the tuning combination parameter α in the experiments.