Yu Du, Yonggang Lu, Ligang Zhao
Abstract In the field of image classification, deep learning has become the focus of research. But when the number of training samples is small, especially when there are a large number of intra-class variations in the small samples, the performance of deep learning is often not satisfactory. To deal with the problem, a new dictionary learning method based on sparse representation and an improved coefficient’s constraint is proposed. A general dictionary is learned to eliminate noise signal, and then based on the general dictionary, a class specific dictionary is learned by an improved coefficient’s constraint which maintaining the independence of the dictionary atoms between-classes, while allowing the dependence of the dictionary atoms intra- class. The class specific dictionary combined with the general dictionary is used for the image recognition. Experimental results show that, compared with the state-of-the-art dictionary learning methods, the proposed method usually shows better performance on image classification with small data sets.