Performance evaluation metrics for object-based video segmentation

In this paper we investigate performance metrics for quantitative evaluation of object-based video segmentation algorithms. The metrics address the case when ground-truth video object planes are available. The proposed metrics are used to evaluate three essentially different approaches for video segmentation, i.e., an edge-based [1], a motion clustering based [2], and a total feature vector clustering based [3] algorithm.