作　　者： ; ; ; ;

机构地区： 通信与信息工程学院

出　　处： 《西安邮电大学学报》 2016年第1期59-62,共4页

摘　　要： 结合图像分块与惰性多示例学习(MIL)给出一种鞋印识别新算法。将整个鞋印图像当作包,根据脚底生物特征比例,采用均匀网格分块的方法将鞋印图像分成15个子块,并提取每个子块的纹理与形状特征,当作包中的示例,将鞋印图像识别问题转化成MIL问题;然后,将推土机距离(EMD)应用到K最近邻(KNN)算法中,得出一种惰性MIL新方法用于鞋印识别。在包含5种不同类型花纹的鞋印库中进行实验,识别正确率可达91.28%,较之基于欧氏距离的KNN算法,识别精度平均提高4.0%。 A novel shoeprint recognition algorithm is proposed based on image block and lazy multi-instance learning （MIL）. Firstly, the algorithm regards every shoeprint image as a bag. Then according to the proportion of foot biometrics, a uniform grid partitioning method is used to divide shoeprint image into 15 sub-blocks, and the texture and shape features of each block are extracted. Therefore the shoeprint image recognition problem is transform into a MIL problem. Secondly, the earth mover＇s distance （EMD） is introduced into the traditional k-nearest neighbour （KNN） method to improve the accuracy of image similarity measure, and a new lazy MIL algorithm is designed for shoe prints recognition. Experimental results on shoeprint images datasets where contains five different types of shoeprint pattern indicate that the recognition accuracy of the new method can reach 91.280%, and that compared with Euclidean distance based KNN algorithm, its average recognition accuracy is improved by 4 %.

关 键 词： 多示例学习 鞋印图像识别 纹理 形状特征

领　　域： [自动化与计算机技术] [自动化与计算机技术]