On The Surplus Value of Semantic Video Analysis Beyond the Key Frame
Cees G. M. Snoek, Marcel Worring, Jan‐Mark Geusebroek, D.C. Koelma, F.J. Seinstra · 2005
Typical semantic video analysis methods aim for classification of camera shots based on extracted features from a single keyframe only. In this paper, we sketch a video analysis scenario and evaluate the benefit of analysis beyond the key frame for semantic concept detection performance. We developed detectors for a lexicon of 26 concepts, and evaluated their performance on 120 hours of video data. Results show that, on average, detection performance can increase with almost 40% when the analysis method takes more visual content into account.