Abstract
Image segmentation quality evaluation is a key element when comparing segmentation algorithms. In computer vision, unsupervised segmentation algorithms, although of great interest, often suffer from lack of a well-defined measure to evaluate. This paper presents a novel idea for evaluating such algorithms. A measure is proposed to evaluate four well referred segmentation algorithms. The metric proposed in this work is composed of both size and boundary of segments. When compared with some of the existing techniques, it is found that the proposed scheme can approximate the segmentation error in a better way.
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Dogra, D.P., Majumdar, A.K., Sural, S. (2009). Evaluation of Segmentation Techniques Using Region Size and Boundary Information. In: Chaudhury, S., Mitra, S., Murthy, C.A., Sastry, P.S., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2009. Lecture Notes in Computer Science, vol 5909. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11164-8_46
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DOI: https://doi.org/10.1007/978-3-642-11164-8_46
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