A co-evaluation framework for improving segmentation evaluation

Object segmentation is an important preprocessing step for many target recognition applications. Many segmentation methods have been studied, but there is still no satisfactory effectiveness measure which makes it hard to compare different segmentation methods, or even different parameterizations of a single method. A good segmentation evaluation method not only would enable different approaches to be compared, but could also be integrated within the target recognition system to adaptively select the appropriate granularity of the segmentation which in turn could improve the recognition accuracy. A few stand-alone effectiveness measures have been proposed, but these measures examine different fundamental criteria of the objects, or examine the same criteria in a different fashion, so they usually work well in some cases, but poorly in the others. We propose a co-evaluation framework, in which different effectiveness measures judge the performance of the segmentation in different ways, and their measures are combined by using a machine learning approach which coalesces the results. Experimental results demonstrate that our method performs better than the existing methods.