A Two-phase Color Quantization Approach Based on Spectral Clustering
基于谱聚类的两阶段颜色量化算法

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.