What Is Texture Analysis In Computer Vision?

In computer vision, we are required to deal with the different structural characteristics of image or video data. The texture is one of the major characteristics of this kind of data which is used for identifying objects or regions of interest in an image. In this article, we will have an understanding of texture and texture analysis. We will also discuss some of the important procedures that are required to be followed in the way of texture analysis. The major points to be discussed in this article are listed below.

Table of Contents

  1. What is Texture?  
  2. What is Texture Analysis?
  3. Challenges in Texture Analysis
  4. Feature Extraction Method for Categorizing Textures
    1. Feature extraction  
    2. Classification
  5. Application of Texture Analysis

Let’s start the discussion by understanding the texture.


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What is Texture?

There are two kinds of texture: one is tactile and the other one is optical, where we can feel the tactile texture by touching or seeing the surface. When we talk about the optical or visual texture, it refers to the shape and content of the image. Humans can easily diagnose the texture of the image but making a machine to analyze the texture of the image has its complexity. In the field of image processing, we can consider the spatial changes of the brightness intensity of the pixel as the texture of the image. 

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In image processing, textural images are those images in which a specific pattern of texture distribution is repeated sequentially throughout the image. The below image can be a representation of the textural image where part (b), represents the repeated pattern on the texture.

Going through the above points, we can define and understand the texture. Now the main aim of the article is to understand texture analysis. In the next section, we will see how we can define and generalize the texture analysis. 

What is Texture Analysis?

Till now we have got an understanding of the texture in the image data. The aim of the article is to discuss how machines understand the texture of images so that machines can be capable of performing different tasks of image processing. Understanding the texture of the images requires texture analysis and we can consider texture analysis as a whole subject. In the general view of the textural analysis, we can find the areas like the following.

Considering the above representation, the texture analysis can be categorized into four categories: texture segmentation, texture synthesis, texture classification, texture shape.      

Let’s have a small discussion about the categories so that we will have a proper vision about the areas of image processing where they can be used.

Texture Segmentation: In image data, we can find out the difference between the image areas in the context of the texture. By texture segmentation, we find different boundaries of the different textures in the image. We can also say that, in texture segmentation, we compare different areas of the images if the textual characteristics are different and define them by assigning the boundaries.

Texture Synthesis:  In image synthesis, we use methods to make images that have a similar texture as the images we have as input. This part of the texture analysis is being used in the creation of computer games and image graphics.

Texture Shape Extraction: In this section, we try to extract the 3D view and areas of the images. Normally these areas are covered with a unique or specific texture. This section is useful in analyzing the shape and structure of the objects in the image using the image’s textual properties and spatial relationship of textures with each other.

Texture classification: We can consider it as the most important lesson of texture analysis which is responsible for describing the type of image texture. Texture classification is the process of assigning an unknown sample of textures from the image to any predefined texture class.

Here we have seen a basic introduction to the texture analysis and the parts of the texture analysis. Now we are required to know the challenges we may face in the texture analysis.

Challenges in Texture Analysis  

Talking about the real world, we can face two major challenges in texture analysis. These major challenges are as follows:

  • Rotation image
  • Noise image

We can say that these challenges in texture analysis and image classification can have various destructive effects. So if we are applying texture analysis methods for classification against these challenges, the methods are not sustainable. In practice, the performance level of the process can be reduced severely. We always want to make the analyzing and categorizing process of the images robust and stable while neutralizing the effect of these challenges. 

Also, there can be various chances of images to differ from each other in terms of scale, viewpoint, brightness or intensity of light. Formally, this causes challenges in texture classification. To reduce the effects of the challenges, various methods and logic have been introduced. Also, we can simply classify the texture using feature extraction. Let’s take a look at the feature extraction for categorizing texture.

Feature Extraction Method for Categorizing Textures    

As we have discussed in the above section, the classification of texture is one of the most important parts of texture analysis and the basic idea behind it is to provide the labels to samples of any image according to the class of texture. We can perform the classification using feature extraction from the images. We can split the process into two parts as follows:

  1. The feature extraction part: In this part, we try to extract the textual properties of the images and the motive of this part is to make a model which can deal with every texture of the image that exists in training time.
  1. The classification part: In this part, we perform the texture analysis on the test images with the same techniques which we applied for the training images and apply a classification algorithm which can be a statistic or deep learning algorithm.

The images get examined by the feature extractor and then texture classification is done by the classification algorithm. The basic representation of the procedure can be given by the following image:

Let’s have a look at a more descriptive definition of these two parts.

Feature extraction  

As we have discussed in the above points, the basic idea behind this part is to extract texture features from the images, and for this procedure, we are required to have a model for every texture available in the training images. These features can be discrete histogram, numerical, empirical distribution, and texture features such as contrast, spatial structure, direction, etc. The extracted texture feature can be used for teaching classification. There can be various ways to classify texture and the efficiency of these ways can be dependent on the type of texture features extracted. These methods can be divided into the following groups:

  1. Statistical methods
  2. Structural methods 
  3. Model-based methods
  4. Transformer methods

We can use any of the methods for extracting features from the images. 


In the second stage of the process, we perform classification on the extracted texture features based on the machine learning algorithms with classification algorithms. Using the classification method, appropriate classes for each texture are selected. Using the comparison between the vector of the extracted texture feature from the extraction part of the process and the vector of the selection test phase characteristics, we determine its classes. This step is repeated for every image presented in the. The estimated classes for evaluation with the actual class are adapted and the recognition rate is calculated which shows the efficiency of the implemented algorithm. Normally applied accuracy measure is:

Classification accuracy = (correct matches / number of test image) ×100

Here we have seen how the texture classification can be applied to the images which is an important part of the texture analysis.

Application of Texture Analysis

In the above sections of the article, we have seen that textures present in the image are the precious information that can be utilized for various tasks related to image processing. Some of the tasks and applications that can be performed using texture analysis are as follows:

  1. Face detection
  2. Tracking objects in the videos
  3. Diagnosis of product quality
  4. Medical image analysis
  5. Remote sensing
  6. Vegetation

Final Words 

Here in this article, we discussed the texture related to image data and had an overview of the texture analysis. Along with this, we also had an introduction to texture classification, which is an important part of texture analysis. Finally, we listed a few applications of texture analysis and texture in image and video processing.

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Yugesh Verma
Yugesh is a graduate in automobile engineering and worked as a data analyst intern. He completed several Data Science projects. He has a strong interest in Deep Learning and writing blogs on data science and machine learning.

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