Computer Vision News - March 2019

20 Computer Vision News Focus on Let’s review some of the algorithms included: Isolation Forest - A set of trees is used to partition the data and outliers are determined by looking at the partitioning and seeing how isolated a leaf is in the overall structure. Isolation Forest handles multidimensional data well. Histogram-based Outlier Detection - This is an effective system for handling unsupervised data, it assumes feature independence. The metric used for outlier detection is the construction of histograms and measuring distance from the histogram. It’s much faster than multivariate approaches, but at the cost of lower accuracy. Angle-Based Outlier Detection (ABOD) - The method measures the distance of every data point from its neighbors, taking into account the distance between those neighbors -- the variance of the cosine scores is the metric used for outlier detection. ABOD handles multidimensional data well. PyOD includes 2 versions: 1) Fast -- only using the k nearest neighbors, and 2) Taking into account all data points. k Nearest Neighbors Detector - For each data point, the distances from its k nearest neighbors are looked at for outlier detection. PyOD supports 3 versions of kNN: 1) using the distance from the k-th nearest neighbor as the metric for outlier detection, 2) using the average of the k nearest neighbor distances as the metric, and 3) using the median of the k nearest neighbor distances as the metric. Focus on: PyOD