Texture Analysis and Classification in Computer Vision
Summary
Texture analysis and classification form a cornerstone of modern computer vision, enabling machines to interpret the surface properties and spatial arrangements of patterns in images. By extracting statistical, structural or model-based features, algorithms can distinguish between materials, predict surface roughness or identify objects in diverse contexts such as medical imaging, remote sensing and industrial inspection. Early approaches relied on handcrafted descriptors—such as co-occurrence matrices, Gabor filter responses and local binary patterns—to summarise pixel relationships and local neighbourhood structures. More recently, deep convolutional neural networks have reshaped the field by learning hierarchical representations directly from data, offering superior adaptability to variations in scale, illumination and viewpoint. Contemporary research seeks to integrate the interpretability and efficiency of traditional methods with the representational power of data-driven models, yielding hybrid frameworks that address both the accuracy requirements of safety-critical applications and the computational constraints of real-time deployment.
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Texture Analysis and Classification in Computer Vision publication trend
The graph below shows the total number of articles in texture analysis and classification in computer vision across all publications each year (not limited to Nature Index journals).
Technical terms
Texture: The spatial arrangement of pixel intensities or colours that conveys surface properties and pattern regularities.
Feature extraction: The process of transforming raw image data into a set of numerical descriptors that characterise texture patterns.
Classification: The assignment of an image or region to a predefined category based on its extracted features.
Local Binary Pattern (LBP): A descriptor that encodes local texture by thresholding neighbourhood pixels against a central value to form a binary code.
Gray-Level Co-occurrence Matrix (GLCM): A statistical tool that tabulates the frequency of pairs of pixel intensities at specified spatial offsets, capturing contrast and homogeneity.
References
- 3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors. Applied Sciences (2021).
- A novel method for detecting morphologically similar crops and weeds based on the combination of contour masks and filtered Local Binary Pattern operators. GigaScience (2020).
- Colour and Texture Descriptors for Visual Recognition: A Historical Overview. Journal of Imaging (2021).
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