Arousal content representation of sports videos using dynamic prediction hidden Markov models

This paper develops dynamic prediction hidden Markov models for arousal time curve estimation in sports videos. The method determines the arousal time curve by selecting a state sequence that maximizes the joint probability density function between the states and the arousal time curve. We derive the parameters using the expected maximization algorithm. Experiments were performed on several types of sports videos. Test measures include squared residual error and criteria derived from psychology. The experimental results show that the novel method performed better in estimating the arousal time curve than state of the art linear regression methods on most of the tested sports videos.

[1]  Hang-Bong Kang,et al.  Affective content detection using HMMs , 2003, ACM Multimedia.

[2]  Alan Hanjalic,et al.  Affective video content representation and modeling , 2005, IEEE Transactions on Multimedia.

[3]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[4]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[5]  Xiao-Ping Zhang,et al.  An ICA Mixture Hidden Markov Model for Video Content Analysis , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[6]  Mohammad Soleymani,et al.  Continuous emotion detection in response to music videos , 2011, Face and Gesture 2011.

[7]  Mohammad Soleymani,et al.  A Bayesian framework for video affective representation , 2009, 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops.

[8]  Arthur E. Hoerl,et al.  Ridge Regression: Biased Estimation for Nonorthogonal Problems , 2000, Technometrics.

[9]  Chng Eng Siong,et al.  Automatic Sports Video Genre Classification using Pseudo-2D-HMM , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[10]  Xiaofeng Wang,et al.  Ice hockey shot event modeling with mixture hidden Markov model , 2009, EiMM '09.

[11]  Patrick Gros,et al.  Audiovisual integration for tennis broadcast structuring , 2006, Multimedia Tools and Applications.

[12]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[13]  A. E. Hoerl,et al.  Ridge regression: biased estimation for nonorthogonal problems , 2000 .

[14]  George Eastman House,et al.  Sparse Bayesian Learning and the Relevance Vector Machine , 2001 .

[15]  Thierry Pun,et al.  DEAP: A Database for Emotion Analysis ;Using Physiological Signals , 2012, IEEE Transactions on Affective Computing.