Abstract:

In the content-based image retrieval, Support Vector Machine (SVM) can resolve the problem of small sample size, and the active learning algorithm can select the most optimal samples to learn actively according to the learning process, thus reducing the training time greatly and improving the efficiency of the classification algorithm. In order to obtain the more rapid and efficient image retrieval, a novel image retrieval method based on SVM and active learning is proposed. Firstly, the method constructs the classifier on the basis of SVM. Secondly, the “V” elimination method is used to reduce the sample sets quickly, and the optimal selection method is applied to select the optimal samples from the reduced sample sets as the training ones. Finally, the optimal training sample set with abundant information and lower redundancy is obtained, so that the better SVM based classifier is constructed and the higher retrieval efficiency is achieved. Experimental results show that, compared with the traditional image retrieval method based on SVM and active learning, the proposed method has better performance and can improve the retrieval performance greatly.

Key words: image retrieval;SVM;active learning;“V”elimination method;optimal selection method