Abstract
Kernel methods are becoming increasingly challenging for use in a wide variety of computer vision applications. This paper introduces the use of Kernel Maximum Mean Discrepancy (KMMD) for region merging process. KMMD is a recent unsupervised kernel-based method commonly used in analysing and comparing distributions. We propose a region merging approach based on the KMMD framework which aims at improving the quality of an initial segmentation result. The performance of the proposed method has been compared with four states of the art region merging methods over a test of Berkeley image segmentation data set by means of the probabilistic rand index and variation of information errors. Experiments show that our approach succeeds in achieving a segmentation quality equal to or greater than the referenced methods.
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Slimene, A., Zagrouba, E. (2013). Kernel Maximum Mean Discrepancy for Region Merging Approach. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8048. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40246-3_59
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DOI: https://doi.org/10.1007/978-3-642-40246-3_59
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