Comparative Evaluation of Interactive Segmentation Algorithms Using One Unified User Interactive Type

Abstract

In interactive segmentation, user inputs are required to produce cues for the algorithms to extract the object of interest. Different input types were recommended by the researchers in their developed algorithms. The most common input types are points, strokes and bounding box. Different evaluation parameters were used in the researches in this field for comparison. Our previous work shows that, for non-complex image, segmentation result will not be affected by the user input type used. Complex images are defined as images whereby the colors of the objects of interest and the background are similar and vice-versa. In some of the complex images, parts of the color of the objects of interest are present in the background. This paper extends our previous work by using the proposed unified input types, which consists of a bounding box to locate the object of interest range and a stroke for the foreground, on three interactive segmentation algorithms for non-complex and complex image. Three different evaluation measures are computed to compare the segmentation quality: Variation of Information (VI), Global Consistency Error (GCE) and Jaccard index (JI). From the experiment results, it is noticed that, all three algorithms perform well for non-complex images but could not perform as good for complex images.