In this paper we propose a framework for stereoscopic video shot classification that includes low-level representations exploiting visual and disparity information and determination of optimal discriminant subspaces based on Linear Discriminant Analysis (LDA). Low-level representations are obtained through various color, disparity and texture descriptors which are applied to shot key frames. A new LDA-based subspace representation is proposed aiming at that optimal utilization of both visual and disparity information. The proposed shot classification framework has been evaluated on football stereoscopic videos providing enhanced classification performance and class discrimination, in comparison to using visual information only and standard LDA.
|Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
|Institute of Electrical and Electronics Engineering (IEEE)
|IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2014)
|21/09/14 → 24/09/14
- Shot classification
- stereoscopic video
- Linear Discriminant Analysis (LDA)