Image Retrieval Using Relevance Feedback with Historical Information Learning
The relevance feedback techniques are applied efficiently to the content-based image similarity retrieval(CBIR).However,the traditional relevance feedback techniques do not sufficiently utilize the historical retrieval information.In order to enhance the image retrieval efficiency and precision further,a retrieval approach using the relevance feedback based on the historical retrieval information learning was presented in this paper.Every image retrieval result was saved as the historical retrieval information.When carrying out a new retrieval,decide the semantic similarity between the current query sample and the historical retrieval information,and predict the retrieval results,so as to reduce the interactive number of the relevance feedbacks.The performance of the approach was tested using an image database containing 8000 images.Results show the improved performance compared with the CBIR system with the traditional relevance feedback technique when using the same image similarity measure.