Learning heterogeneous data for hierarchical web video classification

Abstract

Web videos such as YouTube are hard to obtain sufficient precisely labeled training data and analyze due to the complex ontology. To deal with these problems, we present a hierarchical web video classification framework by learning heterogeneous web data, and construct a bottom-up semantic forest of video concepts by learning from meta-data. The main contributions are two-folds: firstly, analysis about middle-level concepts' distribution is taken based on data collected from web communities, and a concepts redistribution assumption is made to build effective transfer learning algorithm. Furthermore, an AdaBoost-Like transfer learning algorithm is proposed to transfer the knowledge learned from Flickr images to YouTube video domain and thus it facilitates video classification. Secondly, a group of hierarchical taxonomies named Semantic Forest are mined from YouTube and Flickr tags which reflect better user intention on the semantic level. A bottom-up semantic integration is also constructed with the help of semantic forest, in order to analyze video content hierarchically in a novel perspective. A group of experiments are performed on the dataset collected from Flickr and YouTube. Compared with state-of-the-arts, the proposed framework is more robust and tolerant to web noise.