融合多颜色空间分量的自适应彩色图像分割

提出了一种新的简单有效的融合多颜色分量的分割方法，首先在六个不同的颜色空间中选择最佳的待分割颜色分量，然后应用直方图和空间模糊C均值（SFCM）技术对不同颜色分量进行自适应初始分割，最后融合分割结果并进行区域合并。利用该算法在Berkeley图像库上进行了大量实验，实验结果表明，与当前一些经典分割算法Mean-shift、FCR、CTM等相比，利用该方法能够获得更好的分割结果以及更优的性能指标。

Adaptive color image segmentation by fusion of multi-color space components

This paper proposes a new, simple, and efficient segmentation approach. It chooses the best segmentation com-ponents among six different color spaces, then Histogram and SFCM technique is applied for initialization of segmenta-tion. It fuses the segmentation results and merge regions. Extensive experiments have been taken on Berkeley image data-base by using the proposed algorithm. The results show that, compared with some classical segmentation algorithms, such as Mean-shift, FCR and CTM, etc, the method can achieve better image partitioning and better performance.