Video annotation by incremental learning from grouped heterogeneous sources

Transfer learning has shown promising results in leveraging loosely labeledWeb images (source domain) to learn a robust classifier for the unlabeled consumer videos (target domain). Existing transfer learning methods typically apply source domain data to learn a fixed model for predicting target domain data once and for all, ignoring rapidly updating Web data and continuously changes of users requirements. We propose an incremental transfer learning framework, in which heterogeneous knowledge are integrated and incrementally added to update the target classifier during learning process. Under the framework, images (image source domain) queried from Web image search engine and videos (video source domain) from existing action datasets are adopted to provide static information and motion information of the target video, respectively. For the image source domain, images are partitioned into several groups according to their semantic information. And for the video source domain, videos are divided in the same way. Unlike traditional methods which measure relevance between the source group and the whole target domain videos, the group weights in this paper are treated as latent variables for each target domain video and learned automatically according to the probability distribution difference between the individual source group and target domain videos. Experimental results on the two challenging video datasets (i.e., CCV and Kodak) demonstrate the effectiveness of our proposed method.