Abstract
Color image segmentation is the process of grouping regions according to some criterium. In this work, we cope with this problem using a graph-based approach based on removal of minimum spanning tree edges, however the tuning of parameters is a difficult task. To better identify the set of parameters which optimizes the error producing good segmentations, we propose the use of genetic algorithm in order to establish the best set of parameters. According to test experiments, our proposed method presents better results when compared to other approaches from the literature.
The authors are grateful to PUC Minas – Pontifícia Universidade Católica de Minas Gerais, CNPq, CAPES and FAPEMIG for the financial support of this work.
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Andrade, A.C., Patrocínio, Z.K.G., Guimarães, S.J.F. (2013). Improving the Quality of Color Image Segmentation Using Genetic Algorithm. In: Petrosino, A. (eds) Image Analysis and Processing – ICIAP 2013. ICIAP 2013. Lecture Notes in Computer Science, vol 8156. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41181-6_16
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