Applying Bio-inspired Techniques to the p-Median Problem

Domínguez, Enrique; Naranjo, Javier

doi:10.1007/11494669_9

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…So the need to assess just to keep only one, judged as best, according to a predefined objective function [13].…”

Section: Unsupervised Image Segmentation Using Sflamentioning

confidence: 99%

Unsupervised Segmentation of Images Based on Shuffled Frog-Leaping Algorithm

2017

J Inf Process Syst

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The image segmentation is the most important operation in an image processing system. It is located at the joint between the processing and analysis of the images. Unsupervised segmentation aims to automatically separate the image into natural clusters. However, because of its complexity several methods have been proposed, specifically methods of optimization. In our work we are interested to the technique SFLA (Shuffled Frog-Leaping Algorithm). It's a memetic meta-heuristic algorithm that is based on frog populations in nature searching for food. This paper proposes a new approach of unsupervised image segmentation based on SFLA method. It is implemented and applied to different types of images. To validate the performances of our approach, we performed experiments which were compared to the method of K-means.

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“…So the need to assess just to keep only one, judged as best, according to a predefined objective function [13].…”

Section: Unsupervised Image Segmentation Using Sflamentioning

confidence: 99%

Unsupervised Segmentation of Images Based on Shuffled Frog-Leaping Algorithm

2017

J Inf Process Syst

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“…In general, this functional is not differentiable and it may have several local minima. Optimization problem (2) can also be found in literature as a k-median problem and it is most frequently solved by various metaheuristic methods [5,16,20].…”

Section: Weighted Clustering Problemmentioning

confidence: 99%

Color image segmentation based on intensity and hue clustering - a comparison of LS and LAD approaches

Taler¹,

Sabo²

2014

Croat. Oper. Res. Rev.

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Abstract. Motivated by the method for color image segmentation based on intensity and hue clustering proposed in [26] we give some theoretical explanations for this method that directly follows from the natural connection between the maximum likelihood approach and Least Squares or Least Absolute Deviations clustering optimality criteria. The method is tested and illustrated on a few typical situations, such as the presence of outliers among the data.

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