Image classification method in limited datasets

Image classification is an essential component in the modern computer vision field, in which dictionary learning–based classification has garnered significant attention due to its robustness. Generally, most dictionary learning algorithms can be optimized through data augmentation and regularization techniques. In data augmentation research, focus is often placed on enhancing features of specific class samples, whereas the impact of shared information among images of different categories is overlooked. High inter-class shared information can make it challenging to differentiate among categories. To tackle this concern, the paper advocates an innovative data augmentation approach. The proposed method reduces excessive similarity within class samples by randomly replacing pixel values, thereby improving the classification performance. Building on this, we designed a joint dictionary learning algorithm that embeds label and local consistency. The basic steps of the proposed algorithm are as follows: (1) generate specific auxiliary samples as training samples, (2) initialize the dictionary and expression coefficients, (3) introduce label constraints and local constraints and update the dictionary, and (4) generate a classifier and classify the test samples. Extensive experiments have demonstrated the efficiency of the proposed approach.