The task considered in this paper is performance evaluation of region segmentation algorithms in the ground-truth-based paradigm. Given a machine segmentation and a ground-truth segmentation, performance measures are needed. We propose to consider the image segmentation problem as one of data clustering and, as a consequence, to use measures for comparing clusterings developed in statistics and machine learning. By doing so, we obtain a variety of performance measures which have not been used before in image processing. In particular, some of these measures have the highly desired property of being a metric. Experimental results are reported on both synthetic and real data to validate the measures and compare them with others.

Abstract. Structural pattern representations, especially graphs, have advantages over feature vectors. However, they also suffer from a number of disadvantages, for example, their high computational complexity. Moreover, we observe that in the field of statistical pattern recognition a number of powerful concepts emerged recently that have no equivalent counterpart in the domain of structural pattern recognition yet. Examples include multiple classifier systems and kernel methods. In this paper, we survey a number of recent developments that may be suitable to overcome some of the current limitations of graph based representations in pattern recognition.

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