Wei Jiang, Courtenay Cotton, Shih-Fu Chang, Dan Ellis, Alexander C. Loui. Short-Term Audio-Visual Atoms for Generic Video Concept Classification. In Proceeding of ACM international conference on Multimedia (ACM MM), October 2009.
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We investigate the challenging issue of joint audio-visual analysis of generic videos targeting at semantic concept detection. We propose to extract a novel representation, the Short-term Audio-Visual Atom (S-AVA), for improved concept detection. An S-AVA is defined as a short-term region track associated with regional visual features and background audio features. An effective algorithm, named Short-Term Region tracking with joint Point Tracking and Region Segmentation (STR-PTRS), is developed to extract S-AVAs from generic videos under challenging conditions such as uneven lighting, clutter, occlusions, and complicated motions of both objects and camera. Discriminative audio-visual codebooks are constructed on top of S-AVAs using Multiple Instance Learning. Codebook-based features are generated for semantic concept detection. We extensively evaluate our algorithm over Kodak's consumer benchmark video set from real users. Experimental results confirm significant performance improvements?over 120% MAP gain compared to alternative approaches using static region segmentation without temporal tracking. The joint audio-visual features also outperform visual features alone by an average of 8.5% (in terms of AP) over 21 concepts, with many concepts achieving more than 20%
@InProceedings{STAVA:ACM2009,
Author = {Jiang, Wei and Cotton, Courtenay and Chang, Shih-Fu and Ellis, Dan and Loui, Alexander C.},
Title = {Short-Term Audio-Visual Atoms for Generic Video Concept Classification},
BookTitle = {Proceeding of ACM international conference on Multimedia (ACM MM)},
Month = {October},
Year = {2009}
}
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