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TASLP
2010

Audio-Based Semantic Concept Classification for Consumer Video

11 years 4 months ago
Audio-Based Semantic Concept Classification for Consumer Video
Abstract--This paper presents a novel method for automatically classifying consumer video clips based on their soundtracks. We use a set of 25 overlapping semantic classes, chosen for their usefulness to users, viability of automatic detection and of annotator labeling, and sufficiency of representation in available video collections. A set of 1, 873 videos from real users has been annotated with these concepts. Starting with a basic representation of each video clip as a sequence of MFCC frames, we experiment with three clip-level representations: Single Gaussian Modeling, Gaussian Mixture Modeling, and Probabilistic Latent Semantic Analysis of a Gaussian Component Histogram. Using such summary features, we produce SVM classifiers based on the Kullback-Leibler, Bhattacharyya, or Mahalanobis distance measures. Quantitative evaluation shows that our approaches are effective for detecting interesting concepts in a large collection of real-world consumer video clips.
Keansub Lee, Daniel P. W. Ellis
Added 21 May 2011
Updated 21 May 2011
Type Journal
Year 2010
Where TASLP
Authors Keansub Lee, Daniel P. W. Ellis
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