Tag suggestion and localization for images by bipartite graph matching

Given an image that is loosely tagged by a few tags, we would like to accurately localize these tags into appropriate image regions, and at the same time suggest new tags for regions if necessary. In this paper, this task is formulated on a bipartite graph, and is solved by finding the best matching between two disjoint sets of nodes. One set of nodes represents regions segmented from an image, and another set represents a combination of existing tags and new candidate tags retrieved from photo sharing platforms. In graph construction, visual characteristics in the representation of the bag of word model and users´ tagging behaviors are jointly considered. By finding the best matching with the Hungarian algorithm, the region-tag correspondence is determined, and tag suggestion and tag localization are accomplished simultaneously. Experimental results show that the proposed unified framework achieves promising image tagging performance.