Improving video classification via youtube video co-watch data
SBNMA '11 Proceedings of the 2011 ACM workshop on Social and behavioural networked media access
Upper body gestures in lecture videos: indexing and correlating to pedagogical significance
Proceedings of the 20th ACM international conference on Multimedia
Categorizing turn-taking interactions
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Semi-supervised multiple instance learning based domain adaptation for object detection
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
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In many classification tasks, the use of expert-labeled data for training is often prohibitively expensive. The use of weakly-labeled data is an attractive solution but raises the problem of label noise. Multiple instance learning, whereby training samples are "bagged" instead of treated as singletons, offers a possible approach to mitigating the effects of label noise. In this paper, we propose the use of MILBoost [28] in a large-scale video taxonomic classification system comprised of hundreds of binary classifiers to handle noisy training data. We test on data with both artificial and real-world noise and compare against the state-of-the-art classifiers based on AdaBoost. We also explore the effects of different bag sizes on different levels of noise on the final classifier performance. Experiments show that when training classifiers with noisy data, MILBoost provides an improvement in performance.