Kernel-induced fuzzy clustering of image pixels with an improved differential evolution algorithm

Das, Swagatam; Sil, Sudeshna

doi:10.1016/j.ins.2009.11.041

Cited by 121 publications

(34 citation statements)

References 57 publications

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“…In 2009, Wang proposed adaptive spatial information-theoretic clustering to be used in image segmentation [43]. In 2010, Yu [44] and Das [45] applied pixel clustering to image segmentation. Simultaneously, Juang employed K-means clustering for segmentation in MRI brain images [46].…”

Section: IImentioning

confidence: 99%

Face Extraction from Image based on K-Means Clustering Algorithms

Farhang¹

2017

ijacsa

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Abstract-This paper proposed a new application of K-means clustering algorithm. Due to ease of implementation and application, K-means algorithm can be widely used. However, one of the disadvantages of clustering algorithms is that there is no balance between the clustering algorithm and its applications, and many researchers have paid less attention to clustering algorithm applications. The purpose of this paper is to apply the clustering algorithm application to face extraction. An improved K-means clustering algorithm was proposed in this study. A new method was also proposed for the use of clustering algorithms in image processing. To evaluate the proposed method, two case studies were used, including four standard images and five images selected from LFW standard database. These images were reviewed first by the K-means clustering algorithm and then by the RER-K-means and FE-RER-clustering algorithms. This study showed that the K-means clustering algorithm could extract faces from the image and the proposed algorithm used for this work increased the accuracy rate and, at the same time, reduced the number of iterations, intra cluster distance, and the related processing time.

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Section: IImentioning

confidence: 99%

Face Extraction from Image based on K-Means Clustering Algorithms

Farhang¹

2017

ijacsa

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“…These optimization tools have mostly been used to improve the performance of important steps of segmentation algorithms. In [15,16], evolutionary and genetic algorithms were used to enhance the clustering process in segmentation. In [17], the authors combined high-level features generated with a visual attention model with low-level features to guide region growing algorithm, where the optimal thresholds of the region growing process were detected using the Particle Swarm Optimization (PSO) algorithm.…”

Section: Related Workmentioning

confidence: 99%

A Swarm Intelligence inspired algorithm for contour detection in images

Kirchmaier

Hawe

Diepold

2013

Applied Soft Computing

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Swarm Intelligence uses a set of agents which are able to move and gather local information in a search space and utilize communication, limited memory, and intelligence for problem solving. In this work, we present an agent-based algorithm which is specifically tailored to detect contours in images. Following a novel movement and communication scheme, the agents are able to position themselves distributed over the entire image to cover all important image positions. To generate global contours, the agents examine the local windowed image information, and based on a set of fitness functions and via communicating with each other, they establish connections. Instead of a centralized paradigm, the global solution is discovered by some principal rules each agent is following. The algorithm is independent of object models or training steps. In our evaluation we focus on boundary detection as a major step towards image segmentation. We therefore evaluate our algorithm using the Berkeley Segmentation Dataset (BSDS) and compare its performance to existing methods via the BSDS benchmark and Pratt's Figure of Merit.

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“…It has been proved that DE is an accurate, reasonably fast, and robust optimizer for many optimization problems in real-world applications such as filter design, PID control, image segmentation, and other scientific and engineering problems [6][7][8][9][10][11][12][13][14][15]. The DE has a similar framework with Genetic Algorithm but only a few control variables.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Binary Differential Evolution with Parameter Adaptation

Jia¹,

Duan²,

Khan³

2013

IJCIS

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Differential Evolution (DE) has been applied to many scientific and engineering problems for its simplicity and efficiency. However, the standard DE cannot be used in a binary search space directly. This paper proposes an adaptive binary Differential Evolution algorithm, or ABDE, that has a similar framework as the standard DE but with an improved binary mutation strategy in which the best individual participates. To further enhance the search ability, the parameters of the ABDE are slightly disturbed in an adaptive manner. Experiments have been carried out by comparing ABDE with two binary DE variants, normDE and BDE, and the most used binary search technique, GA, on a set of 13 selected benchmark functions and the classical 0-1 knapsack problem. Results show that the ABDE performs better than, or at least comparable to, the other algorithms in terms of search ability, convergence speed, and solution accuracy.

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Kernel-induced fuzzy clustering of image pixels with an improved differential evolution algorithm

Cited by 121 publications

References 57 publications

Face Extraction from Image based on K-Means Clustering Algorithms

Face Extraction from Image based on K-Means Clustering Algorithms

A Swarm Intelligence inspired algorithm for contour detection in images

An Efficient Binary Differential Evolution with Parameter Adaptation

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