Abstract:In order to deal with related images and non related images effectively in content based image retrieval (CBIR)

a method of greedy learning based on optimum path forest classification(OPF)

named as GL OPF

was proposed.Firstly

feature vectors of query images and the images in database were extracted by Gabor wavelet transform.Then

the relevance feedback of images was obtained by GL OPF active learning

generating training set of tags.Finally

prototype sets of relevance and unrelated were formed by further evaluation of OPF classifier of mark sets

and the most relevant query images would return after every iteration.The effectiveness of proposed method was verified by experiments on the three image databases Caltch101

Corel and Pascal.The experimental results showed that in eight iterations

the query precision of GL OPF rises more than that of other three methods.In addition

the running and query time of GL OPF is almost the same as that of OPF.