Colour image segmentation with histogram and homogeneity histogram difference using evolutionary algorithms

Kumar, Sushil; Pant, Millie; Kumar, Manoj; Dutt, Aditya S.

doi:10.1007/s13042-015-0360-7

Cited by 23 publications

(4 citation statements)

References 48 publications

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“…The performance of the segmentation algorithms not only depends on the algorithm itself, but also varies with the features. Much effort in the image segmentation research is devoted to feature extraction and representation, such as regional coherence [9], edge smoothness [7], and visual homogeneity [10]. Segmentation results of representative methods are shown in Figure 1.…”

Section: Related Workmentioning

confidence: 99%

Image Segmentation via Multiscale Perceptual Grouping

Feng

2022

Symmetry

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The human eyes observe an image through perceptual units surrounded by symmetrical or asymmetrical object contours at a proper scale, which enables them to quickly extract the foreground of the image. Inspired by this characteristic, a model combined with multiscale perceptual grouping and unit-based segmentation is proposed in this paper. In the multiscale perceptual grouping part, a novel total variation regularization is proposed to smooth the image into different scales, which removes the inhomogeneity and preserves the edges. To simulate perceptual units surrounded by contours, the watershed method is utilized to cluster pixels into groups. The scale of smoothness is determined by the number of perceptual units. In the segmentation part, perceptual units are regarded as the basic element instead of discrete pixels in the graph cut. The appearance models of the foreground and background are constructed by combining the perceptual units. According to the relationship between perceptual units and the appearance model, the foreground can be segmented through a minimum-cut/maximum-flow algorithm. The experiment conducted on the CMU-Cornell iCoseg database shows that the proposed model has a promising performance.

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Section: Related Workmentioning

confidence: 99%

Image Segmentation via Multiscale Perceptual Grouping

Feng

2022

Symmetry

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“…The main difference between GA and DE genetic algorithm result is dependency on crossover operator and in differential evolution algorithm result relies on mutation operator. DE is effectively useful to the many factual optimization and artificial problem such as magnetic bearing, automated mirror design, and mechanical engineering design [13,14]. In the optimization technique, define an objective function for the minimization of task.…”

Section: Differential Evolution Algorithmmentioning

confidence: 99%

Performance Analysis of DE over K-Means Proposed Model of Soft Computing

Patidar¹,

Kumar²,

Kumar³

2016

Advances in Intelligent Systems and Computing

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In real-world data increased periodically, huge amount of data is called Big data. It is a well-known term used to define the exponential growth of data, both in structured and unstructured format. Data analysis is a method of cleaning, altering, learning valuable statistics, decision-making, and advising assumption with the help of many algorithms and procedures such as classification and clustering. In this paper we discuss about big data analysis using soft computing technique and propose how to pair two different approaches like evolutionary algorithm and machine learning approach also try to find better cause.

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“…The main goal of clustering is to find the similarities between every group of data to find common relationships between them. It is widely used in different domains such as medical diagnosis [53], ransomeware detection [54], customer segmentation [49], image processing [33], dental radiography [48], and pattern recognition [34].…”

Section: Introductionmentioning

confidence: 99%

EvoCluster: An Open-Source Nature-Inspired Optimization Clustering Framework

et al. 2021

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EvoCluster is an open source and cross-platform framework implemented in Python language, which includes the most wellknown and recent nature-inspired metaheuristic optimizers that are customized to perform partitional clustering tasks. This paper is an extension to the existing EvoCluster framework in which it includes different distance measures for the objective function, different techniques of detecting the k value, and a user option to consider either supervised or unsupervised datasets. The current implementation of the framework includes ten metaheuristic optimizers, thirty datasets, five objective functions, twelve evaluation measures, more than twenty distance measures, and ten different ways for detecting the k value.

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Colour image segmentation with histogram and homogeneity histogram difference using evolutionary algorithms

Cited by 23 publications

References 48 publications

Image Segmentation via Multiscale Perceptual Grouping

Image Segmentation via Multiscale Perceptual Grouping

Performance Analysis of DE over K-Means Proposed Model of Soft Computing

EvoCluster: An Open-Source Nature-Inspired Optimization Clustering Framework

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