Evaluation of segmentation techniques using region area and boundary matching information

Evaluation techniques play an important role while picking a suitable segmentation scheme out of a number of alternatives. In this paper, a novel supervised segmentation evaluation scheme is proposed that is designed by combining segment area and boundary information. Using the evaluation metric, a ranking of the popular segmentation algorithms is carried out. A comparative analysis with existing supervised metrics that are commonly used for grading segmentation schemes is performed. Experimental results indicate that the performance of the proposed measure is promising.

► Better objective measure for quality evaluation of image segmentation schemes.
► Both boundary and region area play important roles in computation of the measure.
► There exists correlation between subjective and the suggested objective evaluation.