kolian1/texture-segmentation-LBP-vs-GLCM

A Matlab Image segmentation via several feature spaces DEMO
2.1K Downloads
Updated Sun, 30 Aug 2015 18:30:00 +0000

Image segmentation via several feature spaces DEMO.
This demo was designed to demonstrate several commonly used feature spaces, in a segmentation task. It was inspired by multiple questions of Matlab File Exchange users addressed via Matlab Answers, and to author’s personal page and email.
The goal of a segmentation process in image processing is to divide image to elements (segments). When defined and implemented properly, some of the elements may include meaningful elements of the scene- humans, animals, building, vehicles etc. This allows to continue to analyze the image or video stream to get meaningful insight about the filmed scene.
There are multiple segmentation schemes. In short, a classical segmentation scheme is composed of the following stages:
1. Feature space selection, either by hand, using experience and knowledge about problem in hand, or automatic set of features: Harr like Viola Jones windows, LBP, HOG, Color histograms etc…
2. Image transformation to feature space. In many cases a combination of different feature is used.
3. Feature space reduction- to improve both run-time (sometimes dramatically) and segmentation accuracy. PCA is a good example of efficient feature reduction method.
4. Image pixels classification.
5. Spatial/temporal segmentation smoothing- if spatially/temporally continuous segments are assumed.
We have implemented a simplified scheme, omitting some of the above stages.
1. Feature space selection Used feature spaces are: LBP, GLCM, Statistical image moments.
2. Image transformation to feature space. Each feature space is composed of a single feature specified above. In case of color image each color is treated separately, resulting in a larger feature vector. A feature vector if generated of a predefined neighborhood of each chosen pixel. Matab "blockproc" function is used to acquire a feature vector
3. Feature space reduction- to improve both run-time (sometimes dramatically) and segmentation accuracy. PCA is a good example of efficient feature reduction method.
4. Image pixels classification. K-means clustering is chosen du it’s relative simplicity and decent run-time.
5. Not implemented.
By running the demo the user can see various images segmentations achieved by each scheme (differing only in used feature space). Both segmentation quality and run-time are presented.
Note: the scheme is far from perfection, although it achieves decent segmentation. It can be improved in multiple ways- via parameters fitting, utilization of additional feature spaces, better classification schemes, post filtering, usage of more representative color channels etc. Moreover, due to random nature of K-means clustering, different results can be achieved for the same image and parameters. This is not a bug, but an existing scheme drawback.

Cite As

Nikolay S. (2024). kolian1/texture-segmentation-LBP-vs-GLCM (https://github.com/kolian1/texture-segmentation-LBP-vs-GLCM), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2014b
Compatible with any release
Platform Compatibility
Windows macOS Linux

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Versions that use the GitHub default branch cannot be downloaded

Version Published Release Notes
1.3.0.0

To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.