基于协同联想的分类器算法

Classifier algorithm based on synergetic association

文中首先从协同学角度出发 ,提出了一种协同联想记忆分类器 (SAMC)的实现算法。为了验证SAMC算法的有效性 ,针对汽车车牌图像的识别系统 ,构建了一个基于SAMC的解决方案。实验结果表明 ,用SAMC实现算法识别效果好 ,适合于对有噪声的车牌图像的处理 ;该算法训练简单、联想能力强 ,对具有背景噪声和视角引起的图像畸变失真有一定的抗干扰能力 ,识别结果令人满意。

A Synergetic Association Classifier (SAC) based on Synergetic theory is presented, in which a dynamic system is constructed. And by the evolving of the system and finally reaching a steady state, objects could be classified accurately. In this paper, a project which recognizes images of vehicle-license-plate using SAC is also devised. The segmented characters are identified by SAC. The experiments show that the results of the recognition of vehicle-license-plates are satisfying. The algorithm is easier for training and has good capability of association. It is robust for image distortion caused by noise and visual distortion.