High-Level Feature Extraction Using SIFT GMMs and Audio Models

—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.

• Type: Conference
• Year: 2010
• Where: ICPR
• Authors: Nakamasa Inoue, Tatsuhiko Saito, Koichi Shinoda, Sadaoki Furui
• Keywords: Computer Vision, Hidden Markov Models, ICPR 2010, Mel-frequency Cepstral Coefficients, SIFT Gaussian Mixture