ROCK IMAGE RETRIEVAL AND CLASSIFICATION BASED ON GRANULARITY (original) (raw)
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Rock texture retrieval using gray level co-occurrence matrix
2002
ABSTRACT Nowadays, as the computational power increases, the role of automatic visual inspection becomes more important. Therefore, also visual quality control has gained in popularity. This paper presents an application of gray level co-occurrence matrix (GLCM) to texturebased similarity evaluation of rock images. Retrieval results were evaluated for two databases, one consisting of the whole images and the other with blocks obtained by splitting the original images.
Classification of Rock Images using Textural Analysis
Citation/Export MLA Mr. Sachin K. Latad, Prof.(Dr) S. M. Deshmukh, “Classification of Rock Images using Textural Analysis”, March 15 Volume 3 Issue 3 , International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 1323 - 1325, DOI: 10.17762/ijritcc2321-8169.150393 APA Mr. Sachin K. Latad, Prof.(Dr) S. M. Deshmukh, March 15 Volume 3 Issue 3, “Classification of Rock Images using Textural Analysis”, International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 1323 - 1325, DOI: 10.17762/ijritcc2321-8169.150393
COMPARISON OF SOME CONTENT-BASED IMAGE RETRIEVAL SYSTEMS WITH ROCK TEXTURE IMAGES
2002
Texture is commonly used feature in most of the content-based image retrieval systems. This texture retrieval ability can be also applied to rock texture. The retrieval of the rock texture is a demanding task because of special character of rock. In this paper some existing content- based image retrieval systems are tested with a sample set representing clearly different rock
PROBABILISTIC BASED ROCK TEXTURE CLASSIFICATION
The classification of natural images is an essential task in computer vision and pattern recognition applications. Rock images are the typical example of natural images, and their analysis is of major importance in rock industries and bedrock investigations. Rocks are mainly classified into three types. They are Igneous, Metamorphic and Sedimentary. They are further classified into Andesite, Basalt, Amphibolite, Granite, Breccia, Coal and etc… In this project classification is done in three subdivisions. First the given rock image is classified into major class. Next it is classified into subclass. Finally the group of coal images is segmented and classified using Tamura features, Probabilistic Latent Semantic Analysis (PLSA) and Sum of Square Difference classifier. Rock image classification is based on specific visual descriptors extracted from the images. Using these descriptors images are divided into classes according to their visual similarity. This project deals with the rock image classification using two approaches. Firstly the textural features of the rock images are calculated by applying Tamura features extraction method. The Tamura features are Coarseness, Contrast, Directionality, Line likeness, Roughness and Regularity, Smoothness and Angular second moments. In next step calculated Tamura features are applied to Probabilistic Latent Semantic Analysis (PLSA) to generate a topic model. This topic model is applied to SSD classifier to classify the rock image into one of the major class. Similarly the rock textures are classified into subclass, and the group of coal images is segmented and classified. This method is compared with Gray Level Co-occurrence Matrix (GLCM) method and Color Co-occurrence Matrix method. This method gives a better accuracy when compared to those methods. This technique can readily be applied to automatically classify the rocks in such fields of rock industries and bedrock investigations.
Rock image classification using non-homogenous textures and spectral imaging
WSCG proc., WSCG, 2003
Texture analysis and classification are usual tasks in pattern recognition. Rock texture is a demanding classification task, because the texture is often non-homogenous. In this paper, we introduce a rock texture classification method, which is based on textural and spectral features of the rock. The spectral features are considered as some color parameters whereas the textural features are calculated from the co-occurrence matrix. In this classification method, non-homogenous texture images are divided into blocks. The feature values are calculated for each block separately. In this way, the feature values of the texture image can be presented as a feature histogram. The classification method is tested using two types of rock textures. The experimental results show that the proposed features are able to distinguish rock textures quite well.
Textural features for image database retrieval
1998
The decision method involves associating with any pair of images either the class "relevant" or "irrelevant". A Gaussian classifier and nearest neighbor classifier are used. A protocol that translates a frame throughout every image to automatically define for any pair of images whether they are in the relevance class or the irrelevance class is discussed. Experiments on a database of 300 gray scale images with 9,600 groundtruth image pairs showed that the classifier assigned 80% of the image pairs we were sure were relevant, to the relevance class correctly. The actual retrieval accuracy is greater than this lower bound of 80%.
Variogram-Based Descriptors for Comparison and Classification of Rock Texture Images
Mathematical Geosciences, 2019
Rock characterization is typically performed by geologists in mining companies and involves the analysis of several meters of drill-hole samples to describe distinctive geological properties. In this procedure, rock texture is not typically taken into account despite its importance given its close relation with metallurgical responses and, therefore, all mineral processes. To support the work of geology experts, this research seeks to obtain rock texture information, discriminating it from digital images through image processing and machine learning techniques. For this purpose, a geologist-labeled digital photograph database was used with different rock texture classes (including geological textures and structures) from drill-hole samples. To characterize rock texture, three texture descriptors based on variographic information are proposed, which summarize data contained in the image pixels, focusing on local structural patterns that numerically describe its texture properties. Then, based on a methodology of image texture comparison, which could be extended to classify different types of rock texture classes, a quantification of the system's performance was obtained. The results showed a high discrimination among common texture classes using compact variogram-based features that outperformed previous methods applied on the same rock texture database.
Colour and Texture Features for Image Retrieval in Granite Industry
2010
How to cite Complete issue More information about this article Journal's homepage in redalyc.org Scientific Information System Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Non-profit academic project, developed under the open access initiative
Rock Image Classification Using Non-Homogeous Textures and Spectral Imaging
International Conference in Central Europe on Computer Graphics and Visualization, 2003
Texture analysis and classification are usual tasks in pattern recognition. Rock texture is a demanding classification task, because the texture is often non-homogenous. In this paper, we introduce a rock texture classification method, which is based on textural and spectral features of the rock. The spectral features are considered as some color parameters whereas the textural features are calculated from
International Journal of Advanced Computer Science and Applications
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.