Segmenting Video Into Classes of Algorithm-Suitability

Given a set of algorithms, which one(s) should you apply to, i) compute optical flow, or ii) perform feature matching? Would looking at the sequence in question help you decide? It is unclear if even a person with intimate knowledge of all the different algorithms and access to the sequence itself could predict which one to apply. Our hypothesis is that the most suitable algorithm can be chosen for each video automatically, through supervised training of a classifier. The classifier treats the different algorithms as black-box alternative "classes," and predicts when each is best because of their respective performances on training examples where ground truth flow was available. Our experiments show that a simple Random Forest classifier is predictive of algorithm-suitability. The automatic feature selection makes use of both our spatial and temporal video features. We find that algorithm-suitability can be determined per-pixel, capitalizing on the heterogeneity of appearanc...

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Oisin Mac Aodha, Gabriel Brostow, marc Pollefeys
• Keywords: Available Flow Algorithms, Computer Vision, CVPR 2010, Ground Truth Flow, Random Forest Classifier