论文标题
用于异常检测的基于SIFT和冲浪的特征提取
SIFT and SURF based feature extraction for the anomaly detection
论文作者
论文摘要
在本文中，我们建议一种方法，如何使用SIFT和Surf算法提取图像特征以进行异常检测。我们使用这些功能向量在半度审查的现实世界数据集上训练各种分类器（带有少量的样本）方式，并使用大量分类器和使用SVDD和SVM分类器以一类（没有错误的样本）方式（没有错误的样本）方式来训练各种分类器。我们证明，SIFT和SURF算法可以用作特征提取器，可以用来训练半手体监督和一级分类器的精度约为89 \％，并且单级分类器的性能可以与半纯洁的分类器相媲美。我们还公开提供了数据集和源代码。
In this paper, we suggest a way, how to use SIFT and SURF algorithms to extract the image features for anomaly detection. We use those feature vectors to train various classifiers on a real-world dataset in the semi -supervised (with a small number of faulty samples) manner with a large number of classifiers and in the one-class (with no faulty samples) manner using the SVDD and SVM classifier. We prove, that the SIFT and SURF algorithms could be used as feature extractors, that they could be used to train a semi-supervised and one-class classifier with an accuracy around 89\% and that the performance of the one-class classifier could be comparable to the semi-supervised one. We also made our dataset and source code publicly available.
