Compact Sparse Coding for Ground-Based Cloud Classification

Shuang LIU  Zhong ZHANG  Xiaozhong CAO  

IEICE TRANSACTIONS on Information and Systems   Vol.E98-D   No.11   pp.2003-2007
Publication Date: 2015/11/01
Publicized: 2015/08/17
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2015EDL8095
Type of Manuscript: LETTER
Category: Pattern Recognition
ground-based cloud classification,  sparse coding,  compact sparse coding,  

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Although sparse coding has emerged as an extremely powerful tool for texture and image classification, it neglects the relationship of coding coefficients from the same class in the training stage, which may cause a decline in the classification performance. In this paper, we propose a novel coding strategy named compact sparse coding for ground-based cloud classification. We add a constraint on coding coefficients into the objective function of traditional sparse coding. In this way, coding coefficients from the same class can be forced to their mean vector, making them more compact and discriminative. Experiments demonstrate that our method achieves better performance than the state-of-the-art methods.