计算机工程

• 图形图像处理 • 上一篇    下一篇

基于改进GA-RS的火焰图像特征自适应选择

胡燕a,b,王慧琴a,b,黄东宇b,马宗方b   

  1. (西安建筑科技大学 a.管理学院; b.信息与控制工程学院,西安 710055)
  • 收稿日期:2014-08-08 出版日期:2015-08-15 发布日期:2015-08-15
  • 作者简介:胡燕(1981-),女,工程师、博士研究生,主研方向:数字图像处理,信息安全;王慧琴,教授、博士生导师;黄东宇,博士研究生;马宗方,副教授、博士。
  • 基金项目:
    教育部高等学校博士学科点专项科研基金资助项目(20126120110008);陕西省教育厅专项科研计划基金资助项目(14JK1438);西安建筑科技大学人才科技基金资助项目(RC1343)。

Adaptive Selection of Flame Image Features Based on Improved GA-RS

HU Yan  a,b,WANG Huiqin  a,b,HUANG Dongyu  b,MA Zongfang  b   

  1. (a.School of Management; b.School of Information and Control Engineering, Xi’an University of Architecture and Technology,Xi’an 710055,China)
  • Received:2014-08-08 Online:2015-08-15 Published:2015-08-15

摘要: 针对已有固定火焰图像特征模式识别算法泛化能力较差,且误报率较高的问题,提出一种新的火焰图像特征自适应选择算法。根据特征约简的2大基本准则,将遗传优化引入到粗糙集的属性约简,使交叉和变异概率随个体的适应度值自适应调整,以保护较优并淘汰适应度值低 的个体。通过动态修剪并补充新个体增加种群的多样性,从而提高遗传算法的全局寻优能力。实验结果表明,与基于支持向量机的图像型火灾探测算法相比,改进算法在降低特征空间维数的同时,火焰的平均识别率提高了16%。

关键词: 遗传算法, 粗糙集, 属性约简, 全局寻优, 适应度函数

Abstract: Aiming at the lower generalization ability and high false rate of the present pattern recognition algorithms with fixed flame image characteristic,the algorithm of adaptive selection flame image features is proposed in this paper.According to the two basic principles of characteristic reduction,genetic optimization is introduced into the attributes reduction of Rough Set(RS).The ratios of crossover and mutation are changed with individual’s fitness to protect good individual and eliminate bad individual.It dynamically clips the similar individuals and adds new individual,increases the diversity of population to improve the global optimization ability of Genetic Algorithm (GA).Experimental results show that the algorithm can reduce the dimension of feature space,and the average recognition rate of the flame is increased by 16% compared with the image fire detection algorithm based on Support Vector Machine(SVM).

Key words: Genetic Algorithm(GA), Rough Set(RS), attribute reduction, global optimization, fitness function

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