A Powerful and Efficient Method of Image Segmentation Based on Random Forest Algorithm

Faska, Zahra; Khrissi, Lahbib; Haddouch, Khalid; Akkad, Nabil El

doi:10.1007/978-3-030-73882-2_81

Cited by 9 publications

(13 citation statements)

References 23 publications

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“…This proposed method uses a clustering technique based on the ISCA algorithm to find combinations of features that maximize the inter-class distance and minimize the intra-class distance. The goal of our approach is to find an optimal point in the search space that minimizes the fitness function that we formulated in expression (7) in the section below. The search space for each feature, represented by an individual dimension, and the range of each dimension from 0 to 1 are very large and thus require a smart search method.…”

Section: Proposed Clustering Methodsmentioning

confidence: 99%

“…Then, a loop of T (maximum iteration) steps is executed for the consistent clusters. At each iteration of this loop, the score (fitness) of each solution is calculated using the objective function defined in formula (7). After this evaluation phase, the best solution (best ) in the population is determined based on the best score.…”

Section: Proposed Clustering Methodsmentioning

confidence: 99%

“…The performance of the clustering algorithm is evaluated using the fitness function described in equation (7). In this article, each cluster center is considered as a decision variable.…”

Section: Proposed Clustering Methodsmentioning

confidence: 99%

“…The performance of search agents is calculated using the fitness function, which is mentioned in equation (7). The position of the best search agent is updated using the fitness values.…”

Section: Proposed Clustering Methodsmentioning

confidence: 99%

“…As for many segmentation tasks in image processing, the variability from one image to another represents a limit to the performance of segmentation subtlety. Several methods exist, including: split/merge [5], region growing [6], [7], active contouring [8], [9], clustering [9], [10], graphical clipping [11], genetic algorithms [12], etc. Among the many T existing methods, image segmentation by clustering appears to be one of the most used segmentation techniques because of its efficiency and speed.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Performant Clustering Approach Based on An Improved Sine Cosine Algorithm

Khrissi¹,

Akkad²,

Satori³

et al. 2022

IJC

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Image segmentation is a fundamental and important step in many computer vision applications. One of the most widely used image segmentation techniques is clustering. It is a process of segmenting the intensities of a non-homogeneous image into homogeneous regions based on their similarity property. However, clustering methods require a prior initialization of random clustering centers and often converge to the local optimum, thanks to the choices of the initial centers, which is a major drawback. Therefore, to overcome this problem, we used the improved version of the sine-cosine algorithm to optimize the traditional clustering techniques to improve the image segmentation results. The proposed method provides better exploration of the search space compared to the original SCA algorithm which only focuses on the best solution to generate a new solution. The proposed ISCA algorithm is able to speed up the convergence and avoid falling into local optima by introducing two mechanisms that take into account the first is the given random position of the search space and the second is the position of the best solution found so far to balance the exploration and exploitation. The performance of the proposed approach was evaluated by comparing several clustering algorithms based on metaheuristics such as the original SCA, genetic algorithms (GA) and particle swarm optimization (PSO). Our evaluation results were analyzed based on the best fitness values of several metrics used in this paper, which demonstrates the high performance of the proposed approach that gives satisfactory results compared to other comparison methods.

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Section: Proposed Clustering Methodsmentioning

confidence: 99%

Section: Proposed Clustering Methodsmentioning

confidence: 99%

“…The performance of the clustering algorithm is evaluated using the fitness function described in equation (7). In this article, each cluster center is considered as a decision variable.…”

Section: Proposed Clustering Methodsmentioning

confidence: 99%

“…The performance of search agents is calculated using the fitness function, which is mentioned in equation (7). The position of the best search agent is updated using the fitness values.…”

Section: Proposed Clustering Methodsmentioning

confidence: 99%