Improving video classification via youtube video co-watch data
John R. Zhang, Yang Song, Thomas Leung · 2011
Classification of web-based videos is an important task with many applications in video search and ads targeting. However, collecting labeled data needed for classifier training may be prohibitively expensive. Semi-supervised learning provides a possible solution whereby inexpensive but noisy weakly-labeled data is used instead. In this paper, we explore an approach which exploits YouTube video co-watch data to improve the performance of a video taxonomic classification system. A graph is built whereby edges are created based on video co-watch relationships and weakly-labeled videos are selected for classifier training through local graph clustering. Evaluation is performed by comparing against classifiers trained using manually labeled web documents and videos. We find that data collected through the proposed approach can be used to train competitive classifiers versus the state of the art, particularly in the absence of expensive manually-labeled data.