Image segmentation consists of subdividing an image into its constituent parts and extracting those parts of interest (objects). Due to its importance in image analysis, many research works have been conducted for this process. After 40 years of development, a large number of image (and video) segmentation techniques have been proposed and utilized in various applications (Zhang, 2006). With many algorithms developed, some efforts have been spent also on their evaluation, and these efforts have resulted around 100 evaluation papers that can be found in literature for the last century. Several studies have been made in the past in attempt to characterize these existing evaluation methods (Zhang, 1993; Zhang, 1996; Zhang 2001). Segmentation evaluation methods can be classified into analytical methods and empirical methods (Zhang, 1996). The analysis methods treat the algorithms for segmentation directly by examining the principle of algorithms while the empirical methods judge the segmented image (according to predefined criteria or comparing to reference image) so as to indirectly assess the performance of algorithms. Empirical evaluation is practically more effective and usable than analysis evaluation (Zhang, 1996). Recent advancements for segmentation evaluation are mainly made by the development of empirical evaluation techniques. After providing a list of evaluation criteria and methods proposed in the last century as background, this article will provide a summary of the recent (in 21st century) research works for empirical evaluation of image segmentation. These new research works are classified into three groups: (1) those based on existing techniques, (2) those made with modifications of existing techniques, and (3) those that used dissimilar ideas than that of existing techniques. A comparison of these evaluation methods is made before going to the future trends and conclusion.
Boundary IoU: Improving Object-Centric Image Segmentation Evaluation
