A fast MAP adaptation technique for gmm-supervector-based video semantic indexing systems
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Multimodal video concept detection via bag of auditory words and multiple kernel learning
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
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We propose a statistical framework for high-level feature extraction that uses SIFT Gaussian mixture models (GMMs) and audio models. SIFT features were extracted from all the image frames and modeled by a GMM. In addition, we used mel-frequency cepstral coefficients and ergodic hidden Markov models to detect high-level features in audio streams. The best result obtained by using SIFT GMMs in terms of mean average precision on the TRECVID 2009 corpus was 0.150 and was improved to 0.164 by using audio information.