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Indexed by:会议论文

Date of Publication:2016-01-04

Included Journals:EI

Volume:9516

Page Number:675-685

Abstract:The co-occurrence features learned through pattern mining methods have more discriminative power to separate images from other categories than individual low-level features. However, the “pattern explosion ?problem involved in mining process prevents its application in many visual tasks. In this paper, we propose a novel scheme to learn discriminative features based on a mined optimal pattern model. The proposed method deals with the “pattern explosion ?problem from two aspects, (1) it uses selected weak semantic patches instead of grid patches to substantially reduce the database to mine; (2) the adopted optimal pattern model can produce compact and representative patterns which make the resulted image code more effective and discriminative for classification. In our work, we apply the minimal description length (MDL) to mine the optimal pattern model. We evaluate the proposed method on two publicly available datasets (15-Scenes and Oxford-Flowers17) and the experimental results demonstrate its effectiveness. © Springer International Publishing Switzerland 2016.