Compact Sparse Coding for Ground-Based Cloud Classification

Shuang LIU, Zhong ZHANG, Xiaozhong CAO
2015 IEICE transactions on information and systems  
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 groundbased cloud classification. We add a constraint on coding coefficients into the objective function of traditional sparse coding. In this way, coding
more » ... ficients 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.
doi:10.1587/transinf.2015edl8095 fatcat:iva2gd7dvvbhbpkm6f3ma56noy