Strategy of active learning support vector machine for image retrieval

Abstract

This study proposes a new method for content-based image retrieval by finding an optimal classifier. The optimal classifier is achieved by a new active learning support vector machine (SVM) which combines the model selection with the active learning. The unlabelled samples close to the boundary of the SVM classifier are selected based on the feature similarity for the active learning, and the adaptive regularisation is used to select the optimal model. The combination of model selection with active learning accelerates the convergence of the classifier. The new method can improve the image retrieval accuracy and reduce the time consumption. The experimental results show that the proposed method has a better performance with fewer samples and less time consumption for image retrieval.