Color texture classification method based on a statistical multi-model and geodesic distance

Maliani, Ahmed Drissi El; Berthoumieu, Yannick; Aboutajdine, Driss

doi:10.1016/j.jvcir.2014.06.004

Cited by 17 publications

(3 citation statements)

References 20 publications

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“…The use of join vectors and meta-features 63 , 64 would benefit feature exploration by reducing redundancy (for example between B, Z and b* as shown in the results) and pre-selecting relevant color or textural properties for subsequent segmentation. Consequently, the proposed methodology will be able to explore more possible feature vectors by reducing the combinatorial space.…”

Section: Part Iii: Discussionmentioning

confidence: 99%

Reduced and stable feature sets selection with random forest for neurons segmentation in histological images of macaque brain

Bouvier

Souedet

Lévy

et al. 2021

Sci Rep

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In preclinical research, histology images are produced using powerful optical microscopes to digitize entire sections at cell scale. Quantification of stained tissue relies on machine learning driven segmentation. However, such methods require multiple additional information, or features, which are increasing the quantity of data to process. As a result, the quantity of features to deal with represents a drawback to process large series or massive histological images rapidly in a robust manner. Existing feature selection methods can reduce the amount of required information but the selected subsets lack reproducibility. We propose a novel methodology operating on high performance computing (HPC) infrastructures and aiming at finding small and stable sets of features for fast and robust segmentation of high-resolution histological images. This selection has two steps: (1) selection at features families scale (an intermediate pool of features, between spaces and individual features) and (2) feature selection performed on pre-selected features families. We show that the selected sets of features are stables for two different neuron staining. In order to test different configurations, one of these dataset is a mono-subject dataset and the other is a multi-subjects dataset to test different configurations. Furthermore, the feature selection results in a significant reduction of computation time and memory cost. This methodology will allow exhaustive histological studies at a high-resolution scale on HPC infrastructures for both preclinical and clinical research.

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Section: Part Iii: Discussionmentioning

confidence: 99%

Reduced and stable feature sets selection with random forest for neurons segmentation in histological images of macaque brain

Bouvier

Souedet

Lévy

et al. 2021

Sci Rep

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show abstract

“…In our work, Gaussian, Rayleigh, Log-Normal, Inverse-Gaussian, Weibull, and alpha-stable distributions have been used for modeling. These distributions have been chosen because they have been used in many state of the art texture analysis publications [24,[42][43][44][45].…”

Section: Statistical Modelingmentioning

confidence: 99%

Skewed alpha-stable distribution for natural texture modeling and segmentation in contourlet domain

Sarafrazi

Yazdi

2019

J Image Video Proc.

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Texture modeling is a very useful tool in image analysis. This model can be used in texture segmentation, denoising or texture synthesis. In this work, alpha-stable distribution has been proposed to model and segment textured images in contourlet domain. Contourlet transform's ability to extract texture features in different scales and directions combined with alpha-stable distribution's modeling capabilities prove to be an effective method for texture feature extraction. Kolmogorov-Smirnov distance has been used to evaluate how well the proposed distribution fits to the image in the contourlet domain. The performance of the proposed features on image segmentation has been also compared with that of features extracted using different texture analysis methods in the presence of noise. Experimental results have demonstrated the superior performance of the proposed features and their robust performance in the presence of noise.

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“…Since magnitudes are positive values, it is obvious that all of this positive distributions are more suitable than a GGD to take advantage of the exponential family distributions and model the marginal behavior of complex wavelet coefficients histograms as well. Additionally, Weibull distribution has been successfully used to model magnitudes of complex wavelet coefficients in some previous texture retrieval models [22], [24], and [26]- [28]. Angles of complex coefficients are generally exploited using circular distributions such as Wrapped Cauchy and von Mises to characterize the relative phase [29], [30].…”

Section: Introductionmentioning

confidence: 99%

A Finite Mixture of Weibull-Based Statistical Model for Texture Retrieval in the Complex Wavelet Domain

Rami

Maliani

2019

IEEE Access

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This paper presents a new statistical model for texture retrieval in the complex wavelet domain. For this purpose, a finite mixture of Weibull distributions (MoWbl) is proposed to characterize the statistical distribution of magnitudes of complex wavelet coefficients. Despite the ability of the mixture model on capturing a wide range of distribution shapes, choosing an appropriate number of mixture components is a challenging task. To this end, we adopt an unsupervised learning of the model parameters based on the Figueiredo-Jain algorithm and maximum-likelihood estimates. As found in all retrieval statistical-based frameworks, the presence of a similarity measure is trivial. Generally, the failure of a retrieval mixture based system is closely related to the choice of the similarity measure that relies mainly on approximations of some divergences and distances. To overcome this limitation, we propose a canonical form of Weibull distribution which allows us to develop an analytic expression of Cauchy-Schwarz divergence (CSD) for MoWbl distributions. Experiments, conducted on three popular datasets, show that the proposed model yields better performance in terms of goodness-of-fit, retrieval, and execution time compared to some related statistical models for texture retrieval.INDEX TERMS Texture retrieval, statistical analysis, finite mixture of weibulls, Cauchy-Schwarz divergence.

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Color texture classification method based on a statistical multi-model and geodesic distance

Cited by 17 publications

References 20 publications

Reduced and stable feature sets selection with random forest for neurons segmentation in histological images of macaque brain

Reduced and stable feature sets selection with random forest for neurons segmentation in histological images of macaque brain

Skewed alpha-stable distribution for natural texture modeling and segmentation in contourlet domain

A Finite Mixture of Weibull-Based Statistical Model for Texture Retrieval in the Complex Wavelet Domain

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