Algorithms to Detect Anomalies in Images

Modern applications are generating enormous amounts of image data. And in the last years, researchers 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.

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.

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.