Fuzzy clustering for colour reduction in images

Schaefer, Gerald; Zhou, Huiyu

doi:10.1007/s11235-008-9143-8

Cited by 56 publications

(28 citation statements)

References 21 publications

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“…However, a number of faster FCM variants have been developed and have also been shown to work well for colour quantisation [14].…”

Section: Fuzzy C-means Variantsmentioning

confidence: 99%

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Soft computing-based colour quantisation

Schaefer

2014

J Image Video Proc

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Soft computing techniques have shown much potential in a variety of computer vision and image analysis tasks. In this paper, an overview of recent soft computing approaches to the colour quantisation problem is presented. Colour quantisation is a common image processing technique to reduce the number of distinct colours in an image. Those selected colours form a colour palette, while the resulting image quality is directly determined by the choice of colours in the palette. The use of generic optimisation techniques such as simulated annealing and soft computing-based clustering algorithms founded on fuzzy and rough set ideas to formulate colour quantisation algorithms is discussed. These methods are capable of deriving good colour palettes and are shown to outperform standard colour quantisation techniques in terms of image quality. Furthermore, a hybrid colour quantisation algorithm which combines a generic optimisation approach with a common clustering algorithm is shown to lead to improved image quality. Finally, it is demonstrated how optimisation-based colour quantisation can be employed in conjunction with a more appropriate measure for image quality.

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“…However, a number of faster FCM variants have been developed and have also been shown to work well for colour quantisation [14].…”

Section: Fuzzy C-means Variantsmentioning

confidence: 99%

“…Anisotropic mean shift-based FCM (AMSFCM) is an efficient approach to fuzzy c-means clustering which utilises an anisotropic mean shift algorithm coupled with fuzzy clustering [14]. Mean shift-based techniques have been shown to be capable of estimating the local density gradients of similar pixels.…”

Section: Fuzzy C-means Variantsmentioning

confidence: 99%

Soft computing-based colour quantisation

Schaefer

2014

J Image Video Proc

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“…Colour quantization can also be seen as a clustering problem where the task is to identify those clusters that best represent the colours in an image. Schaefer and Zhou [2] investigate the performance of various fuzzy c-means clustering algorithms for colour quantization of images. In particular, they use Conventional fuzzy c-means as well as some more efficient variants thereof, namely fast fuzzy c-means with random sampling, fast generalized fuzzy c-means, and a recently introduced anisotropic mean shift based fuzzy cmeans algorithm.…”

Section: Scanning Through the Issuementioning

confidence: 99%

Guest editorial special issue on intelligent multimedia computing

Emary

Hassanien

2008

Telecommun Syst

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“…Since these methods involve iterative or stochastic optimization, they can obtain higher quality results when compared to preclustering methods at the expense of increased computational time. Clustering algorithms adapted to color quantization include hard c-means [19][20][21][22], competitive learning [23][24][25][26][27], fuzzy c-means [28][29][30][31][32], and self-organizing maps [33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Hard versus fuzzy c-means clustering for color quantization

Wen

Çelebi

2011

EURASIP J. Adv. Signal Process.

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Color quantization is an important operation with many applications in graphics and image processing. Most quantization methods are essentially based on data clustering algorithms. Recent studies have demonstrated the effectiveness of hard c-means (k-means) clustering algorithm in this domain. Other studies reported similar findings pertaining to the fuzzy c-means algorithm. Interestingly, none of these studies directly compared the two types of c-means algorithms. In this study, we implement fast and exact variants of the hard and fuzzy c-means algorithms with several initialization schemes and then compare the resulting quantizers on a diverse set of images. The results demonstrate that fuzzy c-means is significantly slower than hard c-means, and that with respect to output quality, the former algorithm is neither objectively nor subjectively superior to the latter.

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Fuzzy clustering for colour reduction in images

Cited by 56 publications

References 21 publications

Soft computing-based colour quantisation

Soft computing-based colour quantisation

Guest editorial special issue on intelligent multimedia computing

Hard versus fuzzy c-means clustering for color quantization

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