Classification of Handwritten Numbers with Labeled Projective Dictionary Pair Learning.

Dictionary learning is a cornerstone of image classification. We set out to address a longstanding challenge in using dictionary learning for classification; that is to simultaneously maximise the discriminability and sparse-representability power of the learned dictionaries. Upon this premise, we designed class-specific dictionaries incorporating three factors: discriminability, sparsity and classification error. We integrated these metrics into a unified cost function and adopted a new feature space, i.e., histogram of oriented gradients (HOG), to generate the dictionary atoms. The rationale of using HOG features for designing the dictionaries is their strength in describing fine details of crowded images. The results of applying this method in the classification of Chinese handwritten characters showed that the proposed method leads to enhanced classification performance $(\sim98\%)$ compared to other methods such as SqueezeNet, GoogLeNet and MobileNetV2, but with a fraction of parameters. Furthermore, the combination of the HOG features with dictionary learning enhances the accuracy by $11\%$ compared to the case where only pixel domain data are used.