Abstract
Image segmentation is a crucial stage in image processing and pattern recognition. In this paper, color uniformity is considered as a significant criterion for partioning the image into considerable multiple disjoint regions and the distribution of the pixel intensities are investigated in different color spaces. A study of single component and hybrid color components is performed. As a result, it is noticed that different color spaces can be created and the performance of an image segmentation procedure is known to be very much dependent on the choice of the color space. In this study, a novel complex hybrid color space HCbCr is derived from the basic primary color spaces and then transformed it into LUV color space. Further, an unsupervised k-means clustering has been applied which significantly describes the relationship between the color space and the impact on color image segmentation.We experiment our proposed color space image segmentation model with the standard human segmented images of Berkeley dataset, results proved to be very promising compared to conventional and existing color space models.
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Mahantesh, K., Aradhya, V.N.M., Niranjan, S.K. (2014). An Impact of Complex Hybrid Color Space in Image Segmentation. In: Thampi, S., Abraham, A., Pal, S., Rodriguez, J. (eds) Recent Advances in Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 235. Springer, Cham. https://doi.org/10.1007/978-3-319-01778-5_8
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DOI: https://doi.org/10.1007/978-3-319-01778-5_8
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