Abstract:

Since the current content-based image retrieval with the relevance feedback (RF) methods based on the evolutionary algorithm could not well combine the user bias and need to set many parameters, a relevance feedback image retrieval method based on the improved teaching-learning-based optimization algorithm (ITLBO-RF) is proposed. Considering the situation of image retrieval, a series of improvements are implemented. Firstly, combining with the nearest-neighbor approach, the fitness function with constraint is proposed for better reflecting the user bias. Secondly, the center of the relevant images is regarded as the teacher in the teacher phase and the relevant image is regarded as the learning object in the learner phase, which make the algorithm converge fast to the region of relevant images. Finally, the selection operation of students based on Deb standards is conducted. ITLBO-RF is compared with three stateoftheart RFs based on the evolutionary algorithm on two benchmark images. The results show that ITLBO-RF has obvious advantage in comparison with other three algorithms, increases the performance of image retrieval and can better meet the user needs of image retrieval.