期刊
PATTERN RECOGNITION
卷 48, 期 10, 页码 3180-3190出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2015.02.001
关键词
LBP structure learning; Scene recognition; Face recognition; Dynamic texture recognition; Maximal conditional mutual information
资金
- Singapore National Research Foundation under its International Research Centre @ Singapore
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.
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