STUDY OF SOCIAL IMAGE RE-RANKING ACCORDING INTER AND INTRA USER IMPACT Tag-based Image Retrieval,

Abstract

Social media sharing websites like Flickr allow users to annotate images with free tags, which significantly
contribute to the development of the web image retrieval and organization. Tag-based image search is an important
method to find images contributed by social users in such social websites. However, how to make the top ranked result
relevant and with diversity is challenging. In this paper, we propose a social re-ranking system for tag-based image
retrieval with the consideration of image’s relevance and diversity. We aim at re-ranking images according to their visual
information, semantic information and social clues. The initial results include images contributed by different social
users. Usually each user contributes several images. First we sort these images by inter-user re-ranking. Users that have
higher contribution to the given query rank higher. Then we sequentially implement intra-user re-ranking on the ranked
user’s image set, and only the most relevant image from each user’s image set is selected. These selected images compose
the final retrieved results. We build an inverted index structure for the social image dataset to accelerate the searching
process.