摘要
为了避免核匹配追踪通过贪婪算法在基函数字典中寻找一组基函数的线性组合来逼近目标函数的计算量大的缺陷,本文利用免疫克隆选择算法全局最优和局部快速收敛的特性,加快对核匹配追踪算法每次的匹配过程进行优化,提出了一种免疫克隆核匹配追踪图像目标识别算法,该算法有效降低了核匹配追踪算法的计算量,对UCI数据集和遥感图像进行的仿真实验结果表明,相比标准核匹配追踪,该算法保持相当识别率情况下可以明显缩短一次匹配追踪的时间,尤其当字典规模较大时效果更为明显;同基于遗传算法优化相比,本文方法目标识别速度快,精度高。
In order to avoid the default of the greedy algorithm to Approximate given function by searching a linear combination of basis functions choosing from a redundant basis function dictionary for the Kernel Matching Pursuits （KMP Immune Clonal ）, we make use of the global optimal searching ability and the locally quickly searching ability of Selection Algorithm （ICSA） to speed up searching basic function data in function dictionary. And a method for object recognition of Kernel matching pursuits based on immune clonal selection algorithm is presented. This method reduces greatly computer time of the KMP algorithm. The simulation result of the UCI datasets, remote images and Brodatz images show the proposed algorithm can decrease obviously training time leave the classification accuracy almost unchanged, standard KMP. The method has higher classification pursuits based on Genetic Algorithm （GA）. especially for the large size datasets as compared with the speed and more accurate recognition rate over the matching
出处
《电子与信息学报》
EI
CSCD
北大核心
2008年第5期1104-1108,共5页
Journal of Electronics & Information Technology
基金
国家“十一五”预研项目(51307040103)
国家“863”计划项目(20060101Z1119)资助课题
关键词
图像目标识别
核匹配追踪
贪婪算法
免疫克隆选择算法
遗传算法
Image object recognition
Kernel matching pursuits
Greedy algorithm
Immune Clonal SelectionAlgorithm（ICSA）
Genetic Algorithm（GA）
