Online multi-layer dictionary pair learning for visual classification

Classifier training plays an important role in image classification, while a good classifier could more effectively exploit the discriminative information of input features to separate the difficult samples. Inspired by the recent advance of representation based classifiers and the success of multi-layer architectures in visual recognition, we propose a multi-layer dictionary pair learning based classifier to enhance the image classification performance. With the multi-layer structure and a nonlinear feature transform in each layer, the proposed classifier learning model could accumulate stronger discrimination capability than the previous single-layer representation based classifiers. Furthermore, to make our learning model applicable to datasets with a larger amount of samples, we propose an online training algorithm which updates model parameters with data batches. The so-called online multi-layer dictionary pair learning (OMDPL) method is evaluated on benchmark image classification datasets. With the same input features, OMDPL exhibits better classification performance than other popular classifiers.