To improve the performance of image retrieval

and accelerate the speed of image retrieval in content-based image retrieval and reduce the semantic gap between visual low-level features and high-level semantic

relevance feedback image retrieval based on teaching-learning-based optimization algorithm is proposed (TLBO-RF).Considering the specificity of image retrieval and the advantage of the PSO

the update strategy of individual is modified in TLBO

the center of the relevant images is regarded as the teacher and the personal best is introduced

which makes the algorithm converge fast to the region of relevant images that the user is interested in.TLBO-RF is compared to two state-of-the-art RFs based on evolutionary algorithm on two benchmark images.The results show that TLBO-RF has obvious advantage in comparison with other two algorithms

not only increases the performance of image retrieval

but also improves the image retrieval speed

and can better meet the user needs of image retrieval.