Using Implicit Relevane Feedback to Advance Web Image Search

Abstract

Although relevance feedback has been extensively studied in content-based image retrieval in the academic area, no commercial Web image search engine has employed the idea. There are several obstacles for Web image search engines in applying relevance feedback. To overcome these obstacles, we proposed an efficient implicit relevance feedback mechanism. The proposed mechanism shows advantage over traditional relevance feedback methods in the following three aspects. Firstly, instead of enforcing the users to make explicit judgment on the results, our method regards user's click-through data as implicit relevance feedback which release burden from users. Secondly, a hierarchical image search results clustering algorithm is proposed to semantically organize the search results. Using the clustering results as features, our relevance feedback scheme could catch and reflect users' search intention precisely. Lastly, unlike traditional relevance feedback user interface which hardily substitutes subsequent results for previous ones, our method employed friendly recommendation rather than substitution to let the user narrow down on the refined images. To evaluate the implicit relevance feedback mechanism, comprehensive user studies were performed