A Measure of Directional Outlyingness with Applications to Image Data and Video

Abstract

Functional data covers a wide range of data types. They all have in common that the observed objects are functions of of a univariate argument (e.g. time or wavelength) or a multivariate argument (say, a spatial position). These functions take on values which can in turn be univariate (such as the absorbance level) or multivariate (such as the red/green/blue color levels of an image). In practice it is important to be able to detect outliers in such data. For this purpose we introduce a new measure of outlyingness that we compute at each gridpoint of the functions' domain. The proposed Directional Outlyingness} (DO) measure accounts for skewness in the data and only requires O(n) computation time per direction. We derive the influence function of the DO and compute a cutoff for outlier detection. The resulting heatmap and functional outlier map reflect local and global outlyingness of a function. To illustrate the performance of the method on real data it is applied to spectra, MRI images, and video surveillance data.

Cited by in corpus (5)

• On the Nature and Types of Anomalies: A Review of Deviations in Data
• Elastic depths for detecting shape anomalies in functional data
• Outlier detection in non-elliptical data by kernel MRCD
• Independent component analysis for multivariate functional data
• Detecting and Classifying Outliers in Big Functional Data