A clustering method based on multidimensional texture analysis

Hammouche, Kamal; Diaf, Moussa; Postaire, J.‐G.

doi:10.1016/j.patcog.2005.11.024

Cited by 17 publications

(8 citation statements)

References 25 publications

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“…The non-linearity of the 'S' shape in Figure 6(a) is intentionally created to add the complexity of the classification process. Bi-dimensional data sets, Hammouche, et al (2006) 78.7178 97.2484 Table 1. Comparative performance with K-means Clustering.…”

Section: Initial Testmentioning

confidence: 99%

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A Design for Stochastic Texture Classification Methods in Mammography Calcification Detection

Ong¹,

Khoo²

2012

Human-Centric Machine Vision

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Section: Initial Testmentioning

confidence: 99%

“…We also perform a quantitative comparison with the Euclidean distance based K-means clustering (EDKMC) using the image data sets from Hammouche, et al (2006) with a summary of the accuracy shown in Table 1. The proposed method has achieve less classification errors with only 2.7516% as compared to EDKMC errors with 21.2822%.…”

Section: Initial Testmentioning

confidence: 99%

A Design for Stochastic Texture Classification Methods in Mammography Calcification Detection

Ong¹,

Khoo²

2012

Human-Centric Machine Vision

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“…Statistical texture measures can be used to describe the spatial distribution of the data points (Hammouche et al, 2006). Similarly to texture segmentation, the approach consists first of selecting a set of features that characterize the local distribution of the data points in the multidimensional data space in terms of textures.…”

Section: Clustering Based On Multidimensional Texture Analysismentioning

confidence: 99%

Image Processing Techniques for Unsupervised Pattern Classification

Botte-Lecocq¹,

Hammouche²,

Moussa³

et al. 2007

Scene Reconstruction Pose Estimation and Tracking

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“…For the multidimensional data classification methods (Muthanna et al, 2010;Hammouche et al, 2005;Verikas et al, 1997), cluster analysis techniques attempt to separate a set of multidimensional observations into groups or clusters which share some properties of similarity. The objects are generally represented by N-dimensional vectors of observed features.…”

Section: Introductionmentioning

confidence: 99%

A new neural unsupervised classification approach using amended competitive Hebbian learning: PET image segmentation insights

Timouyas

Eddarouich

Hammouch

2017

IJBRA

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This paper proposes a new classification procedure based on the competitive concept, divided into three processing stages. It begins by the estimation of the Probability Density Function (pdf), followed by a competitive training neural network with the Mahalanobis distance as an activation function. This stage allows detecting the local maxima of the pdf. Then, we use the competitive Hebbian learning to analyse the connectivity between the detected maxima of the pdf upon the Mahalanobis distance. The so detected groups of maxima are then used for the classification process. Compared to the K-means clustering or the clustering approaches based on the different competitive learning schemes, the proposed approach has proven, under a number of real (positron emission tomography image) and synthetic data samples, that it does not pass by any thresholding and does not require any prior information on the number of classes or on the structure of their distributions in the data set.Keywords: PET image; positron emission tomography; probability density function; competitive neural networks; Mahalanobis distance; competitive Hebbian learning; topology preserving feature; K-means; classification; competitive concept; thresholding. 2 M. Timouyas, S. Eddarouich and A. HammouchReference to this paper should be made as follows: Timouyas, M., Eddarouich, S. and Hammouch, A. (2017) 'A new neural unsupervised classification approach using amended competitive Hebbian learning: pet image segmentation insights', Int.

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A clustering method based on multidimensional texture analysis

Cited by 17 publications

References 25 publications

A Design for Stochastic Texture Classification Methods in Mammography Calcification Detection

A Design for Stochastic Texture Classification Methods in Mammography Calcification Detection

Image Processing Techniques for Unsupervised Pattern Classification

A new neural unsupervised classification approach using amended competitive Hebbian learning: PET image segmentation insights

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