Learning Discriminative Features for Ground-Based Cloud Classification via Mutual Information Maximization

Shuang LIU  Zhong ZHANG  Baihua XIAO  Xiaozhong CAO  

IEICE TRANSACTIONS on Information and Systems   Vol.E98-D   No.7   pp.1422-1425
Publication Date: 2015/07/01
Publicized: 2015/03/24
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2014EDL8252
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
discriminative patterns,  mutual information maximization,  ground-based cloud classification,  

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Texture feature descriptors such as local binary patterns (LBP) have proven effective for ground-based cloud classification. Traditionally, these texture feature descriptors are predefined in a handcrafted way. In this paper, we propose a novel method which automatically learns discriminative features from labeled samples for ground-based cloud classification. Our key idea is to learn these features through mutual information maximization which learns a transformation matrix for local difference vectors of LBP. The experimental results show that our learned features greatly improves the performance of ground-based cloud classification when compared to the other state-of-the-art methods.