Towards Perceptually Driven Segmentation Evaluation Metrics
To be reliable, an automatic segmentation evaluation metric has to be validated by subjective tests. In this paper, a formal protocol for subjective tests for segmentation quality assessment is presented. The most common artifacts produced by segmentation algorithms are identified and an extensive analysis of their effects on the perceived quality is performed. A psychophysical experiment was performed to assess the quality of video with segmentation errors. The results show how an objective segmentation evaluation metric can be defined as a function of various error types.