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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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Efficient Data Mining for Local Binary Pattern in Texture Image Analysis.

Jin Tae Kwak¹, Sheng Xu¹, Bradford J Wood¹

¹Center for Interventional Oncology, National Institutes of Health Clinical Center, Bethesda MD 20892, USA.

Expert Systems with Applications

|March 14, 2015

Summary

This summary is machine-generated.

This study introduces a data mining approach to efficiently select optimal Local Binary Pattern (LBP) combinations for texture analysis. The method finds discriminative features, improving accuracy and efficiency in image analysis.

Keywords:

classification feature selection frequent pattern mining local binary pattern texture image

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Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Local Binary Pattern (LBP) is a texture descriptor for grayscale images.
Multi-resolution and combined LBPs are effective but selecting optimal parameters is challenging.
Conventional LBP methods face limitations in feature space exploration and efficiency.

Purpose of the Study:

To propose a data mining approach for efficiently exploring high-dimensional LBP feature spaces.
To identify a smaller set of discriminative LBP features for texture analysis.
To maintain computational efficiency while maximizing LBP's potential.

Main Methods:

Developed a data mining strategy to navigate complex LBP feature combinations.
Integrated Local Binary Pattern (LBP), local contrast, and local directional derivative measures.
Utilized three spatial resolutions for feature extraction.
Evaluated the approach on two extensive texture databases.

Main Results:

The data mining approach successfully identified discriminative LBP features.
The proposed method demonstrated effectiveness across different experimental setups.
Robust performance was observed on diverse texture images, validating the approach.

Conclusions:

The data mining approach offers an efficient way to select optimal LBP features for texture analysis.
This method overcomes the limitations of conventional approaches by managing feature space complexity.
The findings highlight the potential for improved accuracy and efficiency in texture recognition systems.