A Survey on Reinforced Similarity Integration in Heterogeneous Image-Rich Information Networks

Web-scale image search engines (e.g., Google image search, Bing image search) mostly rely on surrounding text features. It is difficult for them to interpret users’ search intention only by query keywords and this leads to ambiguous and noisy search results which are far from satisfactory. It is important to use visual information in order to solve the ambiguity in text-based image retrieval. In this paper, we propose a novel Internet image search approach. It only requires the user to click on one query image with minimum effort. Based on both visual and textual content images from a pool are retrieved. In this paper, a system is designed for performing image retrieval in image-rich information networks. In this paper, minimum order k-SimRank to significantly improve the speed of SimRank, one of the most popular algorithms is proposed for computing node similarity in information networks