Clustering of Image Data Set Using K-Means and Fuzzy K-Means Algorithms

Dehariya, Vinod Kumar; Shrivastava, Shailendra Kumar; Jain, R. C.

doi:10.1109/cicn.2010.80

Cited by 109 publications

(50 citation statements)

References 5 publications

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“…Sulaiman and Isa [15] presented a novel clustering algorithm known as Adaptive Fuzzy K-Means for image segmentation with better visual quality. Dehariya et al [16] presented experimental results of both K-Means and Fuzzy K-Means for image segmentation and proved that Fuzzy K-Means is better than K-Means. Rajini and Bhavani [17] focused on improving K-Means and Fuzzy K-Means and applying them to segment MRI brain images.…”

Section: Related Workmentioning

confidence: 99%

“…We made an empirical study besides review of literature to prove that the Fuzzy K-Means exhibits better clustering performance than K-Means. The literature on these two and their comparison besides other derivatives of them [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23] and [24] can be found in section IV.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Experiments on Hypothesis "Fuzzy K-Means is Better than K-Means for Clustering"

Sivarathri¹,

Govardhan²

2014

IJDKP

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experiments on Hypothesis "Fuzzy K-Means is Better than K-Means for Clustering"

Sivarathri¹,

Govardhan²

2014

IJDKP

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“…Data reduction is conducted by translating a multiple attribute description of an object into k membership values, with respect to k classes which represent the fuzzy behaviour. For further details on the fuzzy k-means algorithm, readers are refer to Sulaiman et al [33], Dehariya et al [32], and Jain [34]. In general, the fuzzy k-means classifier uses an iterative procedure that starts with an initial random allocation of the objects to be classified into k clusters.…”

Section: Mapping Algorithm Of River Ice Typesmentioning

confidence: 99%

“…The unsupervised fuzzy k-means classifier was the most common clustering method used in a number of studies that mapped ice cover in Canadian rivers (e.g., [7,[10][11][12]16]. The aim of the fuzzy k-means approach is data reduction, to aid in information transfer in the field of pattern [32][33][34]. Data reduction is conducted by translating a multiple attribute description of an object into k membership values, with respect to k classes which represent the fuzzy behaviour.…”

Section: Mapping Algorithm Of River Ice Typesmentioning

confidence: 99%

Monitoring the Variation in Ice-Cover Characteristics of the Slave River, Canada Using RADARSAT-2 Data—A Case Study

Chu

Das

2015

Remote Sensing

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Abstract:The winter regime of river-ice covers in high northern latitude regions is often a determining factor in the management of water resources, conservation of aquatic ecosystems and preservation of traditional and cultural lifestyles of local peoples. As ground-based monitoring of river-ice regimes in high northern latitudes is expensive and restricted to a few locations due to limited accessibility to most places along rivers from shorelines, remote sensing techniques are a suitable approach for monitoring. This study developed a RADARSAT-2 based method to monitor the spatio-temporal variation of ice covers, as well as ice types during the freeze-up period, along the main channel of the Slave River Delta in the Northwest Territories of Canada. The spatio-temporal variation of ice covers along the river was analyzed using the backscatter-based coefficient of variation (CV) in the 2013-2014 and 2014-2015 winters. As a consequence of weather and flow conditions, the ice cover in the 2013-2014 winter had the higher variation than the 2014-2015 winter, particularly in the potential areas of flooded/cracked ice covers. The river sections near active channels (e.g., Middle Channel and Nagle Channel), Big Eddy, and Great Slave Lake also yielded higher intra-annual variation of ice cover characteristics during the winters. With the inclusion of backscatter and texture analysis from RADARSAT-2 data, four water and ice cover classes consisting of open water, thermal ice, juxtaposed ice, and consolidated ice, were discriminated in the images acquired between November and March in both the studied winters. In addition to river geomorphology and climatic conditions such as river width, sinuosity or air temperature, the fluctuation of water flows during the winter has a significant impact on the variation of OPEN ACCESS Remote Sens. 2015, 7 13665 ice cover as well as the formation of different ice types in the Slave River. The RADARSAT-2 based monitoring algorithm can also be applied to other river systems in high latitude ecosystems to annually monitor their river-ice variation and formation during the freeze-up and ice cover progression period.

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“…The neural network based image segmentation techniques reported in the literature 5 can mainly be classified into two categories: supervised and unsupervised methods. Clustering is an unsupervised learning technique, where one needs to know the number of clusters in advance to classify pixels 6 . A similarity condition is defined between pixels, and then similar pixels are grouped together to form clusters.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Enhanced Independent Component Analysis Approach for Segmentation of Brain Magnetic Resonance Image

Basheera

Ram²

2018

Def. Life. Sc. Jl.

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Medical imaging and analysis plays a crucial role in diagnosis and treatment planning. The anatomical complexity of human brain makes the process of imaging and analyzing very difficult. In spite of huge advancements in medical imaging procedures, accurate segmentation and classification of brain abnormalities remains a challenging and daunting task. This challenge is more visible in the case of brain tumors because of different possible shapes of tumors, locations and image intensities of different types of tumors. In this paper we have presented a method for automated segmentation of brain tumors from magnetic resonance images. An enhanced and modified Gaussian mixture mode model and the independent component analysis segmentation approach has been employed for segmenting brain tumors in magnetic resonance images. The results of segmentation are validated with the help of segmentation evaluation parameters.

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Clustering of Image Data Set Using K-Means and Fuzzy K-Means Algorithms

Cited by 109 publications

References 5 publications

Experiments on Hypothesis "Fuzzy K-Means is Better than K-Means for Clustering"

Experiments on Hypothesis "Fuzzy K-Means is Better than K-Means for Clustering"

Monitoring the Variation in Ice-Cover Characteristics of the Slave River, Canada Using RADARSAT-2 Data—A Case Study

A Hybrid Enhanced Independent Component Analysis Approach for Segmentation of Brain Magnetic Resonance Image

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