A generalized multiclass histogram thresholding approach based on mixture modelling

Boulmerka, Aissa; Állili, Madjid; Ait-Aoudia, Samy

doi:10.1016/j.patcog.2013.09.004

Cited by 16 publications

(8 citation statements)

References 26 publications

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“…In the image processing, the histogram [9][10][11][12][13][14] is a method that associates each intensity value of the number of pixels taking this value. The determination of the histogram is carried out by counting the number of pixels for each image intensity.…”

Section: Methods Based On Histogrammentioning

confidence: 99%

A Novel Feature Extraction Method Based on Histogram and Mathematical Morphology for Isolated Handwritten Greek Characters Recognition

Kessab¹,

Daoui²,

Bouikhalene³

2018

IJAST

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The isolated handwritten character recognition with multiple styles is a challenging research problem. In this paper, we propose a novel method of features extraction for character recognition based on the mathematical morphology and histogram techniques into vertical, horizontal, diagonal and anti-diagonal directions, knowing that the features extarction method is an important step in many image processing tasks. In this context, we present two comparisons in isolated handwritten Greek characters recognition, in fact the first comparison is between the hybrid methods exploited in features extraction which are the mathematical morphology combined with the histogram method; in contrast the second comparison is performed in order to deduce what is the most powerful between third genres of distances used in classification The Euclidean, Manhattan, and Minkowski distances. For this purpose, we have pre-processing each character image with different techniques. Furthermore, in the experiments results we provide extensive comparisons which demonstrate that our method outperforms for different characters' recognition, the results that we have obtained demonstrates really in one hand the performance of a novel method used in features extraction and the Euclidean distance in classification in the other hand.

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Section: Methods Based On Histogrammentioning

confidence: 99%

A Novel Feature Extraction Method Based on Histogram and Mathematical Morphology for Isolated Handwritten Greek Characters Recognition

Kessab¹,

Daoui²,

Bouikhalene³

2018

IJAST

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“…Parametric methods, such as the EM algorithm, are based on estimating the parameters of a given model for the histogram distribution. Recently parametric methods using a mixture of generalized Gaussian distributions were proposed [3,4]. Instead, nonparametric methods aim at optimally thresholding the histogram according to a predetermined criterion [11].…”

Section: Introductionmentioning

confidence: 99%

Multiclass histogram-based thresholding using kernel density estimation and scale-space representations

Korneev¹,

Gilles²,

Battiato³

2022

Preprint

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We present a new method for multiclass thresholding of a histogram which is based on the nonparametric Kernel Density (KD) estimation, where the unknown parameters of the KD estimate are defined using the Expectation-Maximization (EM) iterations. The method compares the number of extracted minima of the KD estimate with the number of the requested clusters minus one. If these numbers match, the algorithm returns positions of the minima as the threshold values, otherwise, the method gradually decreases/increases the kernel bandwidth until the numbers match. We verify the method using synthetic histograms with known threshold values and using the histogram of real X-ray computed tomography images. After thresholding of the real histogram, we estimated the porosity of the sample and compare it with the direct experimental measurements. The comparison shows the meaningfulness of the thresholding.

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“…The supervision can come in the form of pairwise or group relationships as well as partially labelled data. The group constraints and the number of data within each group can be application-driven or generated automatically by initially clustering the data into a large number of groups (Boulmerka, Allili, and Ait-Aoudia 2014;Nouboukpo and Allili 2019). The group constraints are defined at two levels.…”

Section: Introductionmentioning

confidence: 99%

Weakly Semi Supervised learning based Mixture Model With Two-Level Constraints

Nouboukpo

Állili

2021

FLAIRS

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We propose a new weakly supervised approach for classification and clustering based on mixture models. Ourapproach integrates multi-level pairwise group and classconstraints between samples to learn the underlyinggroup structure of the data and propagate (scarce) initial labels to unlabelled data. Our algorithm assumes thenumber of classes is known but does not assume anyprior knowledge about the number of mixture components in each class. Therefore, our model : (1) allocatesmultiple mixture components to individual classes, (2)estimates automatically the number of components ofeach class, 3) propagates class labels to unlabelled datain a consistent way to predefined constraints. Experiments on several real-world and synthetic data datasetsshow the robustness and performance of our model overstate-of-the-art methods.

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A generalized multiclass histogram thresholding approach based on mixture modelling

Cited by 16 publications

References 26 publications

A Novel Feature Extraction Method Based on Histogram and Mathematical Morphology for Isolated Handwritten Greek Characters Recognition

A Novel Feature Extraction Method Based on Histogram and Mathematical Morphology for Isolated Handwritten Greek Characters Recognition

Multiclass histogram-based thresholding using kernel density estimation and scale-space representations

Weakly Semi Supervised learning based Mixture Model With Two-Level Constraints

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