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Color image segmentation based on different color space models using automatic GrabCut.

Dina Khattab1, Hala Mousher Ebied1, Ashraf Saad Hussein2

  • 1Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt.

Thescientificworldjournal
|September 26, 2014
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Summary

This study introduces an automatic GrabCut technique for image segmentation, eliminating user interaction. Experiments show RGB color space yields the best results for the tested images, improving segmentation quality and accuracy.

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Area of Science:

  • Computer Vision
  • Image Processing

Background:

  • Image segmentation is crucial for image analysis.
  • The GrabCut algorithm is a semi-automatic technique requiring user initialization.
  • Automating GrabCut enhances its efficiency and applicability.

Purpose of the Study:

  • To develop and evaluate an automatic GrabCut technique for image segmentation.
  • To compare the performance of automatic GrabCut across various color spaces.
  • To determine the optimal color space for automatic GrabCut segmentation.

Main Methods:

  • Implemented an automatic GrabCut algorithm using unsupervised Orchard-Bouman clustering for initialization.
  • Applied the automatic GrabCut technique to color images using RGB, HSV, CMY, XYZ, and YUV color spaces.
  • Conducted a comparative analysis of segmentation quality and accuracy across different color spaces.

Main Results:

  • The proposed automatic GrabCut technique effectively eliminates user interaction.
  • Comparative analysis revealed significant differences in segmentation performance across color spaces.
  • The RGB color space demonstrated superior performance for the evaluated image dataset.

Conclusions:

  • The automatic GrabCut technique offers an efficient alternative to the semi-automatic version.
  • The choice of color space significantly impacts image segmentation results.
  • RGB is identified as the most effective color space for automatic GrabCut on the tested images.