transfer tagging from image to video
Nowadays massive amount of web video datum has been emerging on the Internet. To achieve an effective and efficient video retrieval, it is critical to automatically assign semantic keywords to the videos via content analysis. However, most of the existing video tagging methods suffer from the problem of lacking sufficient tagged training videos due to high labor cost of manual tagging. Inspired by the observation that there are much more well-labeled data in other yet relevant types of media (e.g. images), in this paper we study how to build a "cross-media tunnel" to transfer external tag knowledge from image to video. Meanwhile, the intrinsic data structures of both image and video spaces are well explored for inferring tags. We propose a Cross-Media Tag Transfer (CMTT) paradigm which is able to: 1) transfer tag knowledge between image and video by minimizing their distribution difference; 2) infer tags by revealing the underlying manifold structures embedded within both image and video spaces. We also learn an explicit mapping function to handle unseen videos. Experimental results have been reported and analyzed to illustrate the superiority of our proposal.
video tagging cross-media tag transfer tag inference