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Learning LBP structure by maximizing the conditional mutual information
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DOI：10.1016/j.patcog.2015.02.001.png)
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Local binary patterns of more bits extracted in a large structure have shown promising results in visual recognition applications. This results in very high-dimensional data so that it is not feasible to directly extract features from the LBP histogram, especially for a large-scale database. Instead of extracting features from the LBP histogram, we propose a new approach to learn discriminative LBP structures for a specific application. Our objective is to select an optimal subset of binarized-pixel-difference features to compose the LBP structure. As these features are strongly correlated, conventional feature-selection methods may not yield a desirable performance. Thus, we propose an incremental Maximal-Conditional-Mutual-Information scheme for LBP structure learning. The proposed approach has demonstrated a superior performance over the state-of-the-arts results on classifying both spatial patterns such as texture classification, scene recognition and face recognition, and spatial-temporal patterns such as dynamic texture recognition. (C) 2015 Elsevier Ltd. All rights reserved.
Keyword:
LBP structure learning
Scene recognition
Face recognition
Dynamic texture recognition
Maximal conditional mutual information
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IF:
7.6
论文数:
1.3W
被引数:
4.5W
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