Image classification based on global dictionary learning method with sparse representation
PU Guolin1, QIU Yuhui2
1. School of Computer Science, Sichuan University of Arts and Science, Dazhou Sichuan 635000, China;
2. College of Computer and Information Science, Southwest University, Chongqing 400715, China
To address the problem of low efficiency for traditional massive image classification, a sparse representation based global dictionary learning method was designed. The traditional dictionary learning steps were distributed to parallel nodes, local dictionaries were first learnt in local nodes and then a global dictionary was updated in real time by those local dictionaries and variables through using convex optimization method, thereby enhancing the efficiency of dictionary learning and classification of massive data. Experiments on the MapReduce platform show that the new algorithm has better performance than classical image classification methods without affecting the classification accuracy, and the new algorithm can be widely used in massive and distributed image classification tasks.
蒲国林, 邱玉辉. 基于稀疏表示全局字典学习的图像分类方法[J]. 计算机应用, 2015, 35(2): 499-501.
PU Guolin, QIU Yuhui. Image classification based on global dictionary learning method with sparse representation. Journal of Computer Applications, 2015, 35(2): 499-501.
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2015, Vol. 35 
