Title :
High-Level Feature Extraction Using SIFT GMMs and Audio Models
Author :
Inoue, Nakamasa ; Saito, Tatsuhiko ; Shinoda, Koichi ; Furui, Sadaoki
Author_Institution :
Dept. of Comput. Sci., Tokyo Inst. of Technol., Tokyo, Japan
Abstract :
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.
Keywords :
Gaussian processes; audio signal processing; audio streaming; cepstral analysis; feature extraction; hidden Markov models; image processing; SIFT Gaussian mixture model; SIFT feature extraction; audio model; audio stream; ergodic hidden Markov model; high-level feature detection; high-level feature extraction; image frame; mean average precision; mel-frequency cepstral coefficient; statistical framework; Computational modeling; Data mining; Detectors; Feature extraction; Hidden Markov models; Streaming media; Visualization; HMM; MFCC; SIFT GMM;
Conference_Titel :
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4244-7542-1
DOI :
10.1109/ICPR.2010.787
