Searching for Recent Celebrity Images in Microblog Platform

With the explosive growth and widespread accessibility of image content in social media, many users are eagerly searching for most recent and relevant images on topics of their interests. However, most current microblog platforms merely make use of textual information, specifically, keywords, for image search which cannot achieve satisfactory results since in most cases image content is inconsistent with textual content. In this paper we tackle this problem under the application of searching for celebrity image. The proposed method is based on the idea of refining the initial text-based search results by utilizing multimedia plus social information. Given a text search query, we first obtain an initial text-based result. Next, we extract a seed tweet set whose images contain faces recognized as celebrities and texts contain the expanded keywords. Third, we extend the seed set based on visual and user information. Lastly, we employ a multi-modal graph based learning method to properly rank the obtained tweets by integrating social and visual information. Extensive experiments on data collected from Tencent Weibo demonstrate that our proposed method could approximately achieve 3-fold improvement in results as compared to the text baseline, typically used in microblog search service.