Classifying offensive sites based on image content
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
Mining temporal patterns of movement for video content classification
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Identifying relevant frames in weakly labeled videos for training concept detectors
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Relevance filtering meets active learning: improving web-based concept detectors
Proceedings of the international conference on Multimedia information retrieval
Pornography detection in video benefits (a lot) from a multi-modal approach
Proceedings of the 2012 ACM international workshop on Audio and multimedia methods for large-scale video analysis
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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%.