Social image search exploiting joint visual-textual information within a fuzzy hypergraph framework

The unremitting growth of social media popularity is manifested by the vast volume of images uploaded to the web. Despite the extensive research efforts, there are still open problems in accurate or efficient image search methods. The majority of existing methods, dedicated to image search, treat the image visual content and the semantic information captured by the social image tags, separately or in a sequential manner. Here, a novel and efficient method is proposed, exploiting visual and textual information simultaneously. The joint visual-textual information is captured by a fuzzy hypergraph powered by the term-frequency and inverse-document-frequency (tf-idf) weighting scheme. Experimental results conducted on two datasets substantiate the merits of the proposed method. Indicatively, an average precision of 77% is measured at 1% recall for image-based queries.