基于AUC优化的非线性主动学习算法及其在障碍物检测中的应用  

A Nonlinear Active Learning Based on AUC Optimization and Its Application to Obstacle Detection

作　　者：韩光[1] 赵春霞[1] 胡雪蕾[1] 

机构地区：[1]南京理工大学计算机科学与技术学院,江苏南京210094

出　　处：《机器人》2010年第3期344-351,共8页Robot

基　　金：国家自然科学基金资助项目(60705020;90820306)

摘　　要：针对障碍物检测中因样本量过大而造成的标记困难以及样本分布不均衡等问题,提出了一种基于AUC优化的非线性主动学习算法.该算法的计算处理过程是:首先利用基于AUC优化的算法在训练集上对非线性分类器进行训练;然后利用已训练好的分类器对所有未标记样本进行分类;接着利用基于AUC优化的样本选择函数计算分类后的样本的得分;最后算法根据分值大小选出最有信息量样本,并且专家根据该样本所在的图像及在图像中位置对其进行标记并放入训练集中.重复上述过程,直到AUC收敛为止.在户外环境图像库上进行了实验,结果表明:该算法能显著减小数据标记的工作量,并能解决因样本分布不平衡而引起的次优解问题,与已有主动学习算法相比性能更优.Aiming at difficulties in labeling caused by a large number of the samples,as well as uneven distribution of the samples in obstacle detection,a nonlinear active learning algorithm based on AUC（area under the receiver operating characteristic） optimization is proposed.Calculation process of this algorithm is as following.Firstly the AUC optimization method is used to train the nonlinear classifier on the training set.Then all the unlabeled samples are classified with the trained classifier.Secondly all the classified samples are scored using the sample selection function based on AUC optimization,and then the best representative samples are selected according to the scores.Finally these samples are labeled by the expert based on the images and location in the image,and then all the labeled samples are put in the training set.The above process is repeated until the AUC converges.Experiments are performed in outdoor environment image database.Experimental results demonstrate that the proposed algorithm can significantly reduce the workload of labeling the samples,and can solve the problem of the sub-optimal solution caused by the uneven sample distribution.The performance is also better than the other active learning algorithms.

关 键 词：障碍物检测 主动学习 AUC优化 非线性分类器 梯度下降法 

分 类 号：TP24[自动化与计算机技术—检测技术与自动化装置] TP18[自动化与计算机技术—控制科学与工程]