Evolutionary Image Segmentation Based on Multiobjective Clustering

This is a Python implementation of the following paper:

Shinichi Shirakawa and Tomoharu Nagao, "Evolutionary Image Segmentation Based on Multiobjective Clustering," Proceedings of the 2009 IEEE Congress on Evolutionary Computation (CEC 2009), pp. 2466-2473 (2009).

If you use this code for your research, please cite our paper:

@inproceedings{ShirakawaCEC2009,
    author = {Shinichi Shirakawa and Tomoharu Nagao},
    title = {Evolutionary Image Segmentation Based on Multiobjective Clustering},
    booktitle = {Proceedings of the 2009 IEEE Congress on Evolutionary Computation (CEC 2009)},
    pages = {2466--2473},
    year = {2009}
}

Requirements

We tested the codes on the following environment:

• Python 3.6.0
• Python package version
  • NumPy 1.16.2
  • SciPy 0.19.1
  • Matplotlib 2.0.2
  • cv2 3.4.0
  • Numba 0.35.0
  • DEAP 1.3

Usage

• Run the python script as python mock_segmentation.py
• In the default setting, the program loads paprika.png as the input image and uses RGB color space
• After execution, the result (output images and a graph) is saved in ./out/
• If you want to use another image file, please add -i option as python mock_segmentation.py -i your_image.png
• If you want to use Lab* color space, please add -c option as python mock_segmentation.py -c Lab
• If you want to run the code with a different setting, please directly modify the script (parameters are set in the beginning of mock_segmentation.py)