Understanding the Local Binary Pattern (LBP): A Powerful Method for Texture Analysis in Computer Vision

The local binary pattern (LBP) is one of the popular texture descriptors used in computer vision. In this article, we will cover the topic of LBP, including an explanation of how the LBP descriptor works and a discussion of its advantages and disadvantages.

LBP is based on appearance features. It is a way to describe the local structure of an image in a way that is invariant to changes in illumination. LBP was first introduced in 1994 and has since been used in a wide range of applications, including object recognition, face detection, and texture classification. Its simplicity and effectiveness make it a popular choice for many computer vision tasks.

The idea behind the LBP

LBP works by comparing the intensity of a central pixel in a small neighborhood with the intensity of its surrounding pixels. Each pixel in the neighborhood is assigned a binary value based on whether its intensity is greater than or less than the intensity of the central pixel (threshold). These binary values are then concatenated into a binary number, which represents the texture of that neighborhood.

These binary values can be then used to construct a histogram of the texture distribution within an image.

Figure 1. illustrates the example of the LBP algorithm. Let’s look into this example to understand the algorithm in detail.

Figure 1: How LBP works

1. Choose a pixel in the image and select its neighboring pixels in a circular or rectangular region around it.
2. Take the threshold (intensity of the selected pixel, here it is 50).
3. Go through every neighboring pixel and check whether its intensity is greater than or less than the threshold.
   Assign 1 to the neighboring pixel, if the intensity of the neighboring pixel is greater than the threshold.
   Assign 0 to the neighboring pixel, if the intensity of the neighboring pixel is less than the threshold.
4. Combine the binary values for all neighboring pixels to obtain a binary code for the central pixel (Anti-clockwise, starting from the top left corner), and convert it to a decimal value.
5. Repeat steps 1–4 for each pixel in the image to obtain a binary code for each pixel.

Now use these LBP values to construct the histogram. By constructing a histogram of the LBP patterns, we can capture the frequency of occurrence of different texture patterns in the image. This histogram can then be used as a feature vector for texture classification tasks, where the goal is to automatically classify images based on their texture properties.

Advantages of LBP

Local Binary Pattern (LBP) has several advantages that make it a popular method for texture analysis in computer vision and image processing:

1. LBP is robust to illumination variations, which means that it can effectively capture texture information in images that have different lighting conditions. This makes it particularly useful for applications such as facial recognition and object detection, where lighting conditions can vary significantly.
2. LBP is a computationally efficient method for texture analysis, which makes it suitable for processing large datasets and real-time applications.
3. LBP is invariant to image rotation and scale. Hence it can effectively capture texture information in images that have been rotated or scaled.
4. LBP has been shown to be highly discriminative for texture analysis

Disadvantages of LBP

While Local Binary Pattern (LBP) has several advantages for texture analysis, it also has some limitations and potential disadvantages, including:

1. LBP is sensitive to noise in the image. This can affect its ability to accurately capture texture information. The LBP operator compares neighboring pixel intensities, and if there is noise in the image, it can result in incorrect binary values that can affect the resulting LBP histogram.
2. LBP only captures local texture information in the immediate vicinity of each pixel, which can limit its ability to capture more global texture information in the image.
3. While LBP is invariant to image rotation, it does not capture rotational information in the texture patterns. This can limit its ability to distinguish between textures that are similar but differ in their rotational patterns.
4. LBP is typically applied to grayscale images, which means that it does not capture color information in the texture patterns.

References

T. Ojala, M. Pietikäinen, and D. Harwood (1994), “Performance evaluation of texture measures with classification based on Kullback discrimination of distributions”, Proceedings of the 12th IAPR International Conference on Pattern Recognition (ICPR 1994), vol. 1, pp. 582–585.