颜色量化是进行图像处理和图像分析的重要技术之一，可以被广泛地应用到图像分割、图像压缩和图像识别中。首先利用高效的二分K均值聚类进行粗略量化，然后使用基于加权距离的谱聚类进行再次量化。实验结果表明，和其他常见量化算法相比，两者的结合使得新方法在运算速度和量化质量上都取得了不错的结果，而加权距离的引入，有效地解决了传统算法将包含像素个数少但重要的颜色进行错划分的问题。

Color quantization or color reduction is an important technique for image analysis and has been widely used in image segmentation, image compression and image recognition. Firstly, on bisecting K-means is used to quantize image roughly and then we refine the image by improved spectral clustering based weighted distance. The stability and quickness of bisecting K-means and adjustable weight make our approach an attractive one. Experimental results show that our approach performs better than octree algorithm in quantized quality and has a less computation complexity than K-means algorithm. For special image, which includes one important color but with only a few pixels, traditional approaches usually lose the important color, but our approach can deal with it by introducing the weight for distances between pixels.