Methods and Algorithms of Cluster Analysis in the Mining Industry - Solution of Tasks for Mineral Rocks Recognition

Baklanova, Olga E.; Shvets, Olga

doi:10.5220/0005022901650171

Cited by 7 publications

(4 citation statements)

References 3 publications

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“…K-means clustering, is a clustering technique that segments the image in K numbers of clusters based on a certain criteria [94]. The K-means technique is one of the most commonly used technique in clustering of images, and it has been applied both in microscopic images [93,95], as well as 3D µCT images of rocks [25].…”

Section: Unsupervised Classificationmentioning

confidence: 99%

X-ray Microcomputed Tomography (µCT) for Mineral Characterization: A Review of Data Analysis Methods

et al. 2019

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The main advantage of X-ray microcomputed tomography (µCT) as a non-destructive imaging tool lies in its ability to analyze the three-dimensional (3D) interior of a sample, therefore eliminating the stereological error exhibited in conventional two-dimensional (2D) image analysis. Coupled with the correct data analysis methods, µCT allows extraction of textural and mineralogical information from ore samples. This study provides a comprehensive overview on the available and potentially useful data analysis methods for processing 3D datasets acquired with laboratory µCT systems. Our study indicates that there is a rapid development of new techniques and algorithms capable of processing µCT datasets, but application of such techniques is often sample-specific. Several methods that have been successfully implemented for other similar materials (soils, aggregates, rocks) were also found to have the potential to be applied in mineral characterization. The main challenge in establishing a µCT system as a mineral characterization tool lies in the computational expenses of processing the large 3D dataset. Additionally, since most of the µCT dataset is based on the attenuation of the minerals, the presence of minerals with similar attenuations limits the capability of µCT in mineral segmentation. Further development on the data processing workflow is needed to accelerate the breakthrough of µCT as an analytical tool in mineral characterization.

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Section: Unsupervised Classificationmentioning

confidence: 99%

X-ray Microcomputed Tomography (µCT) for Mineral Characterization: A Review of Data Analysis Methods

et al. 2019

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“…For more precise classification, [17] proposed methods for extracting color texture features named D_ALBPCSF and G_ALBPCSF obtained by combining statistical and frequency descriptors on direct view images of magmatic and metamorphic rocks. For other classification and indexing algorithms such as, boosting algorithms (LPBoosting) [18], K-nearest neighbors (K-NN) [19], K-means [20] and neural networks [10], features are derived from the measurement of attributes such as energy, entropy, contrast, etc. More repetitively, feature extraction methods are based on three forms of visual attribute analysis: spectral analysis, radiometric analysis and textural analysis in the joint or separate use of color and texture.…”

Section: Introductionmentioning

confidence: 99%

“…A number of segmentation and dictionary learning strategies have been developed in the literature. Suggested methods include: Artificial Neural Networks (ANNs) [13], SVM [25] [26], decision trees [24] [26], K-nearest neighbor (K-NN) [19] [26] [27], K-means [20], boosting algorithms [18], Maximum Likelihood (ML), etc. These methods, for the most part, are combined in a single algorithm.…”

Section: Introductionmentioning

confidence: 99%

New Approach to Rock Classification Based on Sparse Representations

Vangah,

Toa,

Jerôme

et al. 2024

OJAppS

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In geology, classification and lithological recognition of rocks plays an important role in the area of oil and gas exploration, mineral exploration and geological analysis. In other fields of activity such as construction and decoration, this classification makes sense and fully plays its role. However, this classification is slow, approximate and subjective. Automatic classification curbs this subjectivity and fills this gap by offering methods that reflect human perception. We propose a new approach to rock classification based on direct-view images of rocks. The aim is to take advantage of feature extraction methods to estimate a rock dictionary. In this work, we have developed a classification method obtained by concatenating four (4) K-SVD variants into a single signature. This method is based on the K-SVD algorithm combined with four (4) feature extraction techniques: DCT, Gabor filters, D-ALBPCSF and G-ALBPCSF, resulting in the four (4) variants named K-Gabor, K-DCT, KD-ALBPCSF and KD-ALBPCSF respectively. In this work, we developed a classification method obtained by concatenating four (4) variants of K-SVD. The performance of our method was evaluated on the basis of performance indicators such as accuracy with other 96% success rate.

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“…All these characteristics are derived from the measurement of attributes such as energy, contrast, entropy. Then, they are used by various classification and indexing algorithms such as K nearest neighbors (K-NN) [5,8], Boosting algorithms (LPBoosting) [6], Support Vector Machine (SVM) [7,9], Artificial Neural Networks (ANN) [8,10], Maximum Likelihood (MV) [11] and K-means [21] to result in a better classification. In general, the extraction techniques focus on three forms of analysis of visual attribute: spectral analysis, radiometric analysis and textural analysis in the joint or separate use of color and texture.…”

Section: Introductionmentioning

confidence: 99%

Global and Local Characterization of Rock Classification by Gabor and DCT Filters with a Color Texture Descriptor

Vangah¹,

Ouattara²,

Ouattara³

et al. 2019

IJACSA

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In the automatic classification of colored natural textures, the idea of proposing methods that reflect human perception arouses the enthusiasm of researchers in the field of image processing and computer vision. Therefore, the color space and the methods of analysis of color and texture, must be discriminating to correspond to the human vision. Rock images are a typical example of natural images and their analysis is of major importance in the rock industry. In this paper, we combine the statistical (Local Binary Pattern (LBP) with Hue Saturation Value (HSV) and Red Green Blue (RGB) color spaces fusion) and frequency (Gabor filter and Discrete Cosine Transform (DCT)) descriptors named respectively Gabor Adjacent Local Binary Pattern Color Space Fusion (G-ALBPCSF) and DCT Adjacent Local Binary Pattern Color Space Fusion (D-ALBPCSF) for the extraction of visual textural and colorimetric features from direct view images of rocks. The textural images from the two G-ALBPCSF and D-ALBPCSF approaches are evaluated through similarity metrics such as Chi2 and the intersection of histograms that we have adapted to color histograms. The results obtained allowed us to highlight the discrimination of the rock classes. The proposed extraction method provides better classification results for various direct view rock texture images. Then it is validated by a confusion matrix giving a low error rate of 0.8% of classification.

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Methods and Algorithms of Cluster Analysis in the Mining Industry - Solution of Tasks for Mineral Rocks Recognition

Cited by 7 publications

References 3 publications

X-ray Microcomputed Tomography (µCT) for Mineral Characterization: A Review of Data Analysis Methods

X-ray Microcomputed Tomography (µCT) for Mineral Characterization: A Review of Data Analysis Methods

New Approach to Rock Classification Based on Sparse Representations

Global and Local Characterization of Rock Classification by Gabor and DCT Filters with a Color Texture Descriptor

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