Video classification based on social attitudes
Ramya Srinivasan, Amit K. Roy–Chowdhury · 2012
Organizing large video databases is a pressing need and a challenging problem. Social attitudes in the form of users' beliefs and evaluations can benefit classification. For instance, news videos do not gather as much user attention as music videos while sports videos trigger interest mainly during the time of event. In this paper, we provide an extensive analysis of the role of usage statistics in aiding classification. Towards this, we propose a novel framework motivated by evolutionary biology to characterize growth, persistence and decline of contents in online environments. We then incorporate this information in a nearest neighbor classifier to establish categories. The effectiveness of the approach is demonstrated by comparing against results obtained using principal component analysis followed by nearest neighbor based classification.