Motion segmentation by consensus
We present a method for merging multiple partitions into a single partition, by minimising the ratio of pairwise agreements and contradictions between the equivalence relations corresponding to the partitions. The number of equivalence classes is determined automatically. This method is advantageous when merging segmentations obtained independently. We propose using this consensus approach to merge segmentations of features tracked on video. Each segmentation is obtained by clustering on the basis of mean velocity during a particular time interval.