An Impact of Complex Hybrid Color Space in Image Segmentation

  • K. Mahantesh
  • V. N. Manjunath Aradhya
  • S. K. Niranjan
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 235)


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.


Color space models k-means Hybrid color space Image segmentation 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • K. Mahantesh
    • 1
  • V. N. Manjunath Aradhya
    • 2
  • S. K. Niranjan
    • 2
  1. 1.Department of ECESri Jagadguru Balagangadhara Institute of TechnologyBangaloreIndia
  2. 2.Department of MCASri Jayachamarajendra College of EngineeringMysoreIndia

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