Random-sampling-based spatial-temporal feature for consumer video concept classification

Concept classification for consumer videos is a challenging task considering the co-occurrence of a variety objects and arbitrary motions in video segments. In this paper, we present a novel video concept classification framework with random-sampling-based spatialtemporal features. Short-term random-sampled point tracks are obtained within video segments. The spatial-temporal features are extracted from these tracks. Concept codebooks are constructed using Multiple Instance Learning upon the spatial-temporal features. The SVM classifiers are trained over codebook-based histograms for an online concept detection. We performed experiments on a video database taken from YouTube. The experimental results demonstrate that the consumer videos can be efficiently assigned concept labels by our approach.