Spatial Information Based Image Segmentation Using a Modified Particle Swarm Optimization Algorithm

Das, Srikanta; Abraham, Ajith; Konar, Amit

doi:10.1109/isda.2006.253877

Cited by 30 publications

(13 citation statements)

References 21 publications

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“…Das et al [159] introduced a multi-elitist PSO (MEPSO) clustering method based on continuous activation-based encoding scheme and the Xie-Beni index for spatial information based segmentation problem. The effectiveness of MEPSO was presented by comparing it with FVGA.…”

Section: 1 Si N G Le Ob J Ect I Ve Ap P R O a Ch Esmentioning

confidence: 99%

“…Das et al [159,[164][165][166] proposed automatic clustering approaches based on differential evolution (ACDE and its fuzzy version AFDE), where the F-scale and crossover rate parameters were adaptively determined. The representation scheme of ACDE was chosen as continuous activation-based encoding, and the objective functions were selected as the DBI and CH indexes individually.…”

Section: 1 Si N G Le Ob J Ect I Ve Ap P R O a Ch Esmentioning

confidence: 99%

See 1 more Smart Citation

A comprehensive survey of traditional, merge-split and evolutionary approaches proposed for determination of cluster number

Hançer

Karaboğa

2017

Swarm and Evolutionary Computation

108

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Section: 1 Si N G Le Ob J Ect I Ve Ap P R O a Ch Esmentioning

confidence: 99%

Section: 1 Si N G Le Ob J Ect I Ve Ap P R O a Ch Esmentioning

confidence: 99%

A comprehensive survey of traditional, merge-split and evolutionary approaches proposed for determination of cluster number

Hançer

Karaboğa

2017

Swarm and Evolutionary Computation

108

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“…An information entropy technique based on a two-dimensional histogram (image graylevels and means) was also used to find good thresholds by PSO [20]. Also, an unfixed number of thresholds are optimised by PSO, cooperating with Fuzzy means [21]. In general, these methods of optimising thresholds only focus on a single objective.…”

Section: B Image Segmentation Using Psomentioning

confidence: 99%

“…The entropy E has been used for image segmentation [18][19] [21]. When the fitness function E is used for image segmentation (searching for the maximum E), the aim is to find an equal probability for each class.…”

Section: B Fitness Function Using Information Gainmentioning

confidence: 99%

Gaussian mixture models and information entropy for image segmentation using particle swarm optimisation

Johnston

Zhang

2013

2013 28th International Conference on Image and Vision Computing New Zealand (IVCNZ 2013)

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Image segmentation is a key step in image analysis. The Gaussian Mixture Model (GMM) method based on image histograms is popular in image segmentation, but it is difficult to find good parameters of Gaussian models. A hybrid Particle Swarm Optimisation (PSO) algorithm has been used to effectively search the parameters of the models for image segmentation. However, the segmentation results from the fitted models are not stable. In this study, the parameters are optimised by developing a new fitness function in PSO based on information entropy. A combination of the entropy and goodness of the approximation on the image histogram is proposed as a new fitness function further. The results show that the fitness function combining the entropy and goodness of the approximation can be effectively used to obtain better segmentation results than only considering the goodness of the approximation on the image histogram, in terms of intensity errors and the information entropy.

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“…In this section we discuss a new fuzzy clustering algorithm (Das et al, 2006), which can automatically determine the number of clusters in a given dataset. The algorithm is based on a modified PSO algorithm with improved convergence properties.…”

Section: An Automatic Clustering Algorithm Based On Psomentioning

confidence: 99%

Swarm Intelligence Algorithms for Data Clustering

Abraham

Das

Roy³

2008

Soft Computing for Knowledge Discovery and Data Mining

Self Cite

125

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Summary. Clustering aims at representing large datasets by a fewer number of prototypes or clusters. It brings simplicity in modeling data and thus plays a central role in the process of knowledge discovery and data mining. Data mining tasks, in these days, require fast and accurate partitioning of huge datasets, which may come with a variety of attributes or features. This, in turn, imposes severe computational requirements on the relevant clustering techniques. A family of bio-inspired algorithms, well-known as Swarm Intelligence (SI) has recently emerged that meets these requirements and has successfully been applied to a number of real world clustering problems. This chapter explores the role of SI in clustering different kinds of datasets. It finally describes a new SI technique for partitioning any dataset into an optimal number of groups through one run of optimization. Computer simulations undertaken in this research have also been provided to demonstrate the effectiveness of the proposed algorithm.

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Spatial Information Based Image Segmentation Using a Modified Particle Swarm Optimization Algorithm

Abstract: Abstract

Cited by 30 publications

References 21 publications

A comprehensive survey of traditional, merge-split and evolutionary approaches proposed for determination of cluster number

A comprehensive survey of traditional, merge-split and evolutionary approaches proposed for determination of cluster number

Gaussian mixture models and information entropy for image segmentation using particle swarm optimisation

Swarm Intelligence Algorithms for Data Clustering

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