Image Segmentation Via Color Clustering

Abstract: This paper develops a computationally efficient process for segmentation of color images. The input image is partitioned into a set of output images in accordance to color characteristics of various image regions. The algorithm is based on random sampling of the input image and fuzzy clustering of the training data followed by crisp classification of the input image. The user prescribes the number of randomly selected pixels comprising the trainer set and the number of color classes characterizing the image compartments. The algorithm developed here constitutes an effective preprocessing technique with various applications in machine vision systems. Spectral segmentation of the sensor image can potentially lead to enhanced performance of the object detection, classification, recognition, authentication and tracking modules of the autonomous vision system.

Kaveh Heidary. “Image Segmentation Via Color Clustering”. International Journal of Advanced Computer Science and Applications (IJACSA) 5.7 (2014). http://dx.doi.org/10.14569/IJACSA.2014.050702

@article{Heidary2014,
title = {Image Segmentation Via Color Clustering},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2014.050702},
url = {http://dx.doi.org/10.14569/IJACSA.2014.050702},
year = {2014},
publisher = {The Science and Information Organization},
volume = {5},
number = {7},
author = {Kaveh Heidary}
}