Audio-Based Semantic Concept Classification for Consumer Video
Keansub Lee; Daniel P. W. Ellis
- Audio-Based Semantic Concept Classification for Consumer Video
Ellis, Daniel P. W.
- Electrical Engineering
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- IEEE Transactions on Audio, Speech, and Language Processing
- 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 1873 videos from real users has been annotated with these concepts. Starting with a basic representation of each video clip as a sequence of mel-frequency cepstral coefficient (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 support vector machine (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.
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