期刊
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷 36, 期 11, 页码 2173-2184出版社
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2014.2316824
关键词
Dictionary learning; information theory; mutual information; entropy; image classification
资金
- MURI from the Office of Naval Research [N00014-10-1-0934]
We present a two-stage approach for learning dictionaries for object classification tasks based on the principle of information maximization. The proposed method seeks a dictionary that is compact, discriminative, and generative. In the first stage, dictionary atoms are selected from an initial dictionary by maximizing the mutual information measure on dictionary compactness, discrimination and reconstruction. In the second stage, the selected dictionary atoms are updated for improved reconstructive and discriminative power using a simple gradient ascent algorithm on mutual information. Experiments using real data sets demonstrate the effectiveness of our approach for image classification tasks.
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