Content-Enriched Classifier For Web Video Classification

With the explosive growth of online videos, automatic real-time categorization of Web videos plays a key role for organizing, browsing and retrieving the huge amount of videos on the Web. Previous work shows that, in addition to text features, content features of videos are also useful for Web video classification. Unfortunately, extracting content features is computationally prohibitive for real-time video classification. In this paper we propose a novel video classification framework that is able to exploit both content and text features for video classification while avoiding the expensive computation of extracting content features at classification time. The main idea of our approach is to utilize the content features extracted from training data to enrich the text based semantic kernels, yielding content-enriched semantic kernels. The content-enriched semantic kernels enable to utilize both content and text features for classifying new videos without extracting their content features. The experimental results show that our approach significantly out-performs the state-of-the-art video classification methods.