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Audio-Based Semantic Concept Classification for Consumer Video

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2 Author(s)
Keansub Lee ; Electr. Eng. Dept., Columbia Univ., New York, NY, USA ; Ellis, D.P.W.

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.

Published in:

Audio, Speech, and Language Processing, IEEE Transactions on  (Volume:18 ,  Issue: 6 )

Date of Publication:

Aug. 2010

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