图像 异常检测算法
Modern applications are generating enormous amounts of image data. And in the last years, researches began to apply some data mining algorithms to extract useful information from these images to apply smart decisions in business, to detect harmful situations in medicine, to understand behavioral patterns for people, and much more.
现代应用程序正在生成大量图像数据。 在过去的几年中，研究开始应用一些数据挖掘算法来从这些图像中提取有用的信息，以将明智的决策应用于业务，检测医学中的有害情况，了解人们的行为模式等等。
Detecting anomalies play a very important role in data mining which raises suspicions while these outliers most of the time differs a lot from the rest of the majority of images. The purpose of this article is to give a state of the art overview of this topic and give some real examples using two famous algorithms.
检测异常在数据挖掘中起着非常重要的作用，这引起了人们的怀疑，而这些异常值在大多数情况下与大多数其他图像有很大不同。 本文的目的是提供有关此主题的最新技术概述，并使用两种著名的算法给出一些实际示例。
检测图像中的离群值 (Detecting outliers in images)
Detecting outliers in images is not an easy task, and can’t be done efficiently using some famous outlier detection algorithms. Like it will be too hard to detect outlier images using statistical methods like the Z-Score algorithm or Boxplots. While the DBScan clustering algorithm designed when the distribution of values in the feature space cannot be assumed, but applying that algorithm for this application is not clear forward. So the idea is not to choose an algorithm rather than see how to frame the problem correctly then choose one of the algorithms that suit that frame.
检测图像中的离群值不是一件容易的事，并且无法使用某些著名的离群值检测算法来有效地完成。 就像使用Z-Score算法或Boxplots之类的统计方法来检测离群值图像将太困难了。 虽然无法假定DBScan集群算法是在无法假定值在特征空间中的分布时设计的，但尚不清楚该算法在该应用程序中的应用。 因此，这个想法不是选择一种算法，而是看如何正确地解决问题，然后选择一种适合该框架的算法。
Here is a very good article about anomalies detection in common:
这是一篇关于常见异常检测的非常好的文章：
Machine learning researchers have created algorithms such as Isolation forest, one-class SVMs, local outlier factor to detect outliers in images. In the next paper with the title “Anomaly Detection Using Similarity-based One-Class SVM for Network Traffic Characterization”:
机器学习研究人员创建了诸如隔离森林 ， 一类SVM ， 局部离群值因素之 类的算法来检测图像中的离群值。 在下一篇标题为“ 使用基于相似性的一类SVM进行网络流量表征的异常检测 ”的文章中：
