Shannon and Fuzzy entropy based evolutionary image thresholding for image segmentation
Image segmentation is a very important and pre-processing step in image analysis. The conventional multilevel thresholding methods are efficient for bi-level thresholding because of its simplicity, robustness, less convergence time and accuracy. However, a mass of computational cost is needed and ef...
| Autores: | , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | España |
| Recursos: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10230/72687 |
| Acesso em linha: | https://hdl.handle.net/10230/72687 http://dx.doi.org/10.1016/j.aej.2017.05.024 |
| Access Level: | acceso abierto |
| Palavra-chave: | Image segmentation Image thresholding Fuzzy entropy Shannon entropy Particle Swarm Optimization Firefly algorithm |
| Resumo: | Image segmentation is a very important and pre-processing step in image analysis. The conventional multilevel thresholding methods are efficient for bi-level thresholding because of its simplicity, robustness, less convergence time and accuracy. However, a mass of computational cost is needed and efficiency is broken down as an exhaustive search is utilized for finding the optimal thresholds, which results in application of evolutionary algorithm and swarm intelligence to obtain the optimal thresholds. The main aim of image segmentation was to segregate the foreground from background. For the first time this paper established a naturally inspired firefly algorithm based multilevel image thresholding for image segmentation by maximizing Shannon entropy or Fuzzy entropy. The proposed algorithm is tested on standard set of images and results are compared with the Shannon entropy or Fuzzy entropy based methods that are optimized by Differential Evolution (DE), Particle Swarm Optimization (PSO) and bat algorithm (BA). It is demonstrated that the proposed method shows better performance in objective function, structural similarity index, peak signal to noise ratio, misclassification error and CPU time than state of art methods. |
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