A Cognitive Approach to Localized Image Search from the multimedia websites

Abstract: This generation has potential usage of social networking web sites, like face book and picasa, filkcr, YouTube allow users to tag photos, add comment, share the data. The large-scale net genies meta-data not only facilitate users in using and arranging multimedia content, but provide useful statics to improve content searching and management. Personalized search serves as one of such examples where the web search experience is improved by generating the returned list according to the modified user search intents.
In this paper, we exploit the social annotations and propose a novel framework simultaneously considering the user and query relevance to learn to personalized image search. The basic premise is to embed the user preference and query-related search intent into user-specific topic spaces. Since the users’ original annotation is too sparse for topic modeling, we need to enrich users’ annotation pool before user-specific topic spaces construction. The proposed framework contains two components: 1) A Ranking based Multi-correlation Tensor Factorization model is proposed to perform annotation prediction, which is considered as users’ potential annotations for the images; 2) We introduce User-specific Topic Modeling to map the query relevance and user preference into the same user-specific topic space. For performance evaluation, two resources involved with users’ social activities are employed. Experiments on a large-scale Flickr dataset demonstrate the effectiveness of the proposed method.

Keywords: Personalized image search, tensor factorization, topic model, social annotation.