基于颜色纹理描述符的Gabor和DCT滤波器岩石分类的全局和局部表征

IF 0.7 Q3 COMPUTER SCIENCE, THEORY & METHODS International Journal of Advanced Computer Science and Applications Pub Date : 2023-02-16 DOI:10.14569/ijacsa.2019.0100401
J. W. Vangah, S. Ouattara, G. Ouattara, A. Clément
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引用次数: 3

摘要

在有色自然纹理的自动分类中,提出反映人类感知的方法的想法引起了图像处理和计算机视觉领域研究人员的热情。因此,色彩空间和色彩与纹理的分析方法,必须具有辨别性,才能与人的视觉相对应。岩石图像是自然图像的典型代表,其分析在岩石工业中具有重要意义。在本文中,我们将统计(局部二值模式(LBP)与色相饱和度值(HSV)和红绿蓝(RGB)色彩空间融合)和频率(Gabor滤波器和离散余弦变换(DCT))描述符分别命名为Gabor相邻局部二值模式色彩空间融合(G-ALBPCSF)和DCT相邻局部二值模式色彩空间融合(D-ALBPCSF),用于从直接观察图像中提取视觉纹理和色度特征。G-ALBPCSF和D-ALBPCSF两种方法的纹理图像通过相似度指标(如Chi2)和直方图的交集进行评估,我们已经适应了颜色直方图。获得的结果使我们能够突出岩石类别的区别。该方法对各种直视岩石纹理图像具有较好的分类效果。然后通过混淆矩阵进行验证,得出了较低的分类错误率为0.8%。
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Global and Local Characterization of Rock Classification by Gabor and DCT Filters with a Color Texture Descriptor
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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来源期刊
CiteScore
2.30
自引率
22.20%
发文量
519
期刊介绍: IJACSA is a scholarly computer science journal representing the best in research. Its mission is to provide an outlet for quality research to be publicised and published to a global audience. The journal aims to publish papers selected through rigorous double-blind peer review to ensure originality, timeliness, relevance, and readability. In sync with the Journal''s vision "to be a respected publication that publishes peer reviewed research articles, as well as review and survey papers contributed by International community of Authors", we have drawn reviewers and editors from Institutions and Universities across the globe. A double blind peer review process is conducted to ensure that we retain high standards. At IJACSA, we stand strong because we know that global challenges make way for new innovations, new ways and new talent. International Journal of Advanced Computer Science and Applications publishes carefully refereed research, review and survey papers which offer a significant contribution to the computer science literature, and which are of interest to a wide audience. Coverage extends to all main-stream branches of computer science and related applications
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