Solving the label resolution problem in supervised video content classification

Classifying video based on its content often requires learning a classifier from labeled samples. Sometimes the semantic labels available for a video refer to the class or category of the video as a whole; whereas the discriminative features that result in that categorization may only occur over a temporal subset of the video and may occur anywhere, leading to sub-optimal performance. We therefore need to simultaneously learn discriminative features and their temporal support while remaining independent of position within the video. We propose a solution to this label-resolution problem using a wavelet decomposition of frame-level feature time-series followed by learning discrimin ative extrema values over multiple time scales. We apply this approach to automatically detecting the presence of adult content in online videos with a resulting equal-error rate around 5%.