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工程科学与技术:2008,40(5):164-170
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基于免疫数值归约方法的车牌颜色识别算法
(1.四川大学 计算机学院 图像图形研究所,四川 成都 610065;2.中原工学院 计算机科学与技术系,河南 郑州 450007)
Color Recognition of License Plates Based on Immune Data Reduction
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投稿时间:2007-06-11    
中文摘要: 为了解决K近邻算法(KNN)在训练样本较大时计算开销很高的问题,提出了一种新颖的基于免疫原理的数值归约方法,并将之应用于车牌颜色的识别。给出了抗原决定基、免疫细胞等的定义和亲和力计算方法;采用克隆选择与变异、免疫耐受和免疫记忆等机制实现对训练抗原集的多种群并行免疫学习,达到数值归约的目的;利用免疫归约所得检测器结合KNN方法完成免疫应答阶段的车牌颜色识别。在两个数据集上与利用直方图进行数值归约的方法进行了对比实验,结果表明,本文算法能有效进行数值归约,归约率分别达到98.87%和 95.48%;并取得了较好的分类效果,正确率分别为97.45%和94.73%。
Abstract:Conventional KNN (K-nearest Neighbor) algorithm requires large computational consumption when training samples are large. To address this concern, a novel immune-based data reduction approach was proposed and applied to solve color recognition of license plates. Antigenic determinant, immune cells, etc, were defined and computational method of affinity was given. In the process of multi-species parallel learning, clone selection and mutation, immune tolerance, immune memory and other mechanisms were employed to implement data reduction on each training antigen set. Then the reduced representative detectors were used to perform color recognition of license plates during the second immune response stage, integrated with KNN approach. Experiments were conducted on two data sets, compared with three data reduction approaches based on histograms,and results showed that the proposed algorithm can perform data reduction task effectively, with the reduction rates of 98.87% and 95.48%, respectively,and the overall accuracies of 97.45% and 94.73%,
文章编号:20080530     中图分类号:    文献标志码:
基金项目:四川省科技攻关资助项目(07GG006-009)
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引用文本:
王峰,曼丽春,肖逸军.基于免疫数值归约方法的车牌颜色识别算法[J].工程科学与技术,2008,40(5):164-170.
.Color Recognition of License Plates Based on Immune Data Reduction[J].Advanced Engineering Sciences,2008,40(5):164-170.