Abstract
A new framework for adapting common ensemble clustering methods to solve the image segmentation combination problem is presented. The framework is applied to the parameter selection problem in image segmentation and compared with supervised parameter learning. We quantitatively evaluate 9 ensemble clustering methods requiring a known number of clusters and 4 with adaptive estimation of the number of clusters. Experimental results explore the capabilities of the proposed framework. It is shown that the ensemble clustering approach yields results close to the supervised learning, but without any ground truth information.
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Franek, L., Abdala, D.D., Vega-Pons, S., Jiang, X. (2011). Image Segmentation Fusion Using General Ensemble Clustering Methods. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19282-1_30
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DOI: https://doi.org/10.1007/978-3-642-19282-1_30
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