Image Retrieval by User-oriented Ranking

Abstract

Tag-based image search is an important method to process images contributed by social users in social media sharing websites like Flickr. However, existing ranking methods for tag-based image search frequently return results that are irrelevant, low-diversity or time-consuming. In this paper, we propose a user-oriented image ranking system with the consideration of image relevance, diversity and computation complexity, aiming to automatically rank images according to their visual information, semantic information and social clues. When you input a query in the user-oriented image search engine, images tagged with query are obtained as the initial results. The initial results include images contributed by different social users. Usually each user contributes several images. First we sort these users by inter-user ranking. Users that have a higher contribution to the given query rank higher. Then we sequentially implement intra-user ranking on the ranked user's image set, and only the most relevant image in each user's image set is selected. These selected images compose the final retrieval results. Experimental results on Flickr dataset show that our user-oriented ranking method is effective and efficient.