Comparison of three-color image segmentation algorithms in four color spaces

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Abstract
In this paper, we describe how three conventional segmentation methods can be generalized to handle color images. The segmentation algorithms we consider are: (1) seed based region growing, (2) directional derivative based edge detection, and (3) recursive split and merge. We then evaluate the effectiveness of these techniques using color difference metrics associated with four different color spaces and a variety of real and synthetic color images. The four color spaces we evaluate are: (1) the spectral primary system (RGB), (2) the NTSC transmission system (YIQ), (3) the hue saturation and brightness system (HLS), and (4) the CIE perceptually uniform space (LAB). We compare these segmentation results using real and synthetic color images which have been 'hand segmented' to determine true object boundaries.

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