SVMs multi-class loss feedback based discriminative dictionary learning for image classification

The learning model has been popular recently due to its promising results in various image classification tasks. Many existing learning methods, especially the deep learning methods, need a large amount of training data to achieve a high accuracy of classification. Conversely, only provided with a small-size dataset, some dictionary learning (DL) methods can achieve a perfect performance on a image classification task and hence still get a lot of attention. Among these DL methods, DL based feature learning methods are the mainstream for image classification in recent years, however, most of these methods have trained a classifier independently from dictionary learning. Therefore, the features extracted by the learned dictionary may not be very proper to perform classification for the classifier. Inspired by the feedback mechanism in cybernetics, this paper proposes a novel discriminative DL framework, named support vector machines (SVMs) multi-class loss feedback based discriminative dictionary learning (SMLFDL) that learns a discriminative dictionary while training SVMs to make the features extracted by the learned dictionary and SVMs better matched with each other. Because of integrating dictionary learning and SVMs training into a unified learning framework and good exactness of the looped multi-class loss term formulated from the feedback viewpoint for the classification scheme, better classification performance can be achieved. Experimental results on several widely used image databases show that SMLFDL can achieve a competitive performance with other state-of-the-art dictionary learning methods. (C) 2020 Elsevier Ltd. All rights reserved.