Motivation: With the development of social media, a new type of search has become increasingly popular - social search, and this paper particularly focuses on image search in social media. Given a tag query, for example "apple", a good search engine is able to output a set of highly relevant but also diverse images showing fruit apples, cellphone and MacBook. Tag-based social image search always leverages user-generated tags to calculate image's relevance score, however, such tags contain too much noises and it's difficult to form an optimal ranking strategy. Therefore, this paper seeks to simultaneously utilizes tags as well as visual information for image relevance learning.
|Fig 1. Framework of the proposed visual-textual joint relevance learning method.|
|Fig 2. Examples of hyperedge construction. The left figure shows textual-based hyperedges and the right one shows visual-based hyperedges.|