基于YCbCr、RGB和HSV色彩空间的KMCG分割比较研究

F. Alkinani, A. M. Rahma
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引用次数: 1

摘要

Kekre中值码本生成(KMCG)是一种矢量量化算法。它用于几个目的,如图像压缩和分割。通过实际应用,证明了该方法的有效性。本文对KMCG与RGB、YCbCr和HSV三种颜色模型在图像分割中的应用进行了比较研究。实验应用于五幅图像,其中三幅是基准图像。除了视觉结果外,还使用了两个数值指标:E测量和峰值信噪比(PSNR)。结果表明,KMCG与RGB颜色模型相结合,分割效果更好。它返回比使用KMCG与YCbCr或HSV更多的同质片段。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Comparative Study of KMCG Segmentation Based on YCbCr, RGB, and HSV Color Spaces
Kekre Median Codebook Generation (KMCG) is a vector quantization algorithm. It is used for several purposes like image compression and segmentation. It has been applied by several application and shows its efficiency. This paper presents a comparison study of applying KMCG with three color models: RGB, YCbCr, and HSV for image segmentation. The experiments applied on five images, three of them are benchmarks. Two numerical metrics are utilized: E measure and Peak Signal to Noise Ratio (PSNR), in addition to the visual results. The final results show that KMCG conducts better segments when it is applied with the RGB color model. It returns more homogenies segments than using KMCG with YCbCr or HSV.
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