摘要
彩色图像量化是指将一幅具有N种颜色的图像用K(K N)种颜色替换,并使替换后的图像与原图像尽可能接近。文中提出了一种基于核方法的彩色图像量化算法———KCIQ,KCIQ使用核函数将低维输入空间的样本向高维特征空间映射,使得非线性问题变为线性问题。核函数的引入使得不用计算样本在特征空间的具体位置,而只需计算输入空间样本的内积,因而,计算量增加不大。实验表明,该算法对“万绿丛中一点红”这样的特殊图像具有较好的效果,而对普通图像也有和K-均值类似的效果。
CIQ（Color image quantization） refers to the process of replacing N colors in an image with K colors, and making the new image is as much as close to the original one. This paper introduced a KCIQ（Kernel Color Image Quantization） algorithm based on K-means clustering. By using kernel method, data from low-dimensional input space can be mapped to high-dimensional feature space, so that no-linear problem may become linear one in feature space. Since this method involves only inner products of data in input space, it can be evaluated much more efficiently even with no knowledge of mapped data in feature space, Our experiments show that KCIQ performs better than K-means on special image, which includes one important color but with only a few data, and KCIQ has almost the same performance as K-means when working on common images.
出处
《计算机应用》
CSCD
北大核心
2006年第9期2063-2064,2083,共3页
journal of Computer Applications
关键词
彩色图像量化
核方法聚类
核K-均值
CIQ（Color Image Quantization）
kernel method
kernel c-means
