一种用于计算机图形分离的自组织映射彩色量化方法

Improvement to Color Quantization Based on Self-Organizing Maps for Computer Graphics Partition

提出了一种用于计算机图形分离的自组织映射彩色量化改进方法,该方法首先将自组织映射的输出神经元分成灰度组和彩色组分别进行初始化,在训练过程中分别训练灰度组和彩色组中的神经元,同时采用生长、修剪及合并方案来自适应地调整神经网络的结构.实验结果表明,该方法能够大大提高收敛速度和量化精度,满足了后续图像分割和识别的需要.

A novel approach to improve color quantization based on self-organizing maps for computer graphics partition is proposed. According to the attributes of the color images, the output neurons of the self-organizing maps were divided into gray-level group and color group which were initialized separately. The neurons in the gray-level group and the color group were also trained separately in training process. At the same time, the scheme of growing, pruning and merging was utilized to adjust adaptively the structure of network. Experimental results showed that the convergence rate and quantization precision of the neural network were drastically improved. The requirements of the sequential image segmentation and recognition could be satisfied.