Abstract
In this paper, we formulate video tagging as a bipartite graph matching problem. Starting from existing tags that were originally provided by video owners, we conduct keyword-based image search on Flickr. Tags associated with the retrieved images are collected as candidate tags for tag suggestion. Relationships between keyframes extracted from the same video shot and candidate tags are then described as a bipartite graph, and best matching between two disjoint sets is accordingly determined to suggest new tags to this video shot. In constructing the bipartite graph, visual characteristics in terms of the bag of word model and tagging behaviors are jointly considered. Experimental results demonstrate that the proposed features and methodology achieves superior performance over previous approaches.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
