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Color image segmentation using adaptive hierarchical-histogram thresholding.

Min Li1,2, Lei Wang1,2, Shaobo Deng1,2

  • 1Nanchang Institute of Technology, Nanchang, Jiangxi, PR China.

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|January 11, 2020
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Summary
This summary is machine-generated.

A novel Adaptive Hierarchical-Histogram Thresholding (AHHT) algorithm improves color image segmentation by adaptively selecting thresholds. This method offers better results than existing techniques with significantly reduced computational time.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Histogram-based thresholding is crucial for color image segmentation.
  • Selecting optimal thresholds to differentiate objects from background is a key challenge.
  • Existing methods often rely on histogram shape analysis to find thresholds at valleys.

Purpose of the Study:

  • To introduce a novel hierarchical-histogram concept for multi-granularity image abstraction.
  • To present the Adaptive Hierarchical-Histogram Thresholding (AHHT) algorithm for improved threshold selection.
  • To evaluate AHHT's performance against established thresholding techniques.

Main Methods:

  • Developed the hierarchical-histogram concept for image representation.
  • Implemented the Adaptive Hierarchical-Histogram Thresholding (AHHT) algorithm.
  • Compared AHHT with histon-based and roughness-index-based thresholding techniques.

Main Results:

  • AHHT adaptively identifies optimal thresholds from histogram valleys.
  • The algorithm achieved superior segmentation results compared to existing methods.
  • AHHT demonstrated a significant reduction in time complexity.

Conclusions:

  • The hierarchical-histogram approach provides an effective image abstraction.
  • AHHT offers an efficient and accurate solution for color image segmentation.
  • This novel method advances the field of histogram-based image analysis.