Performance Prediction for Unsupervised Video Indexing

Abstract

Recently, performance prediction has been successfully applied in the field of information retrieval for content analysis and retrieval tasks. This paper discusses how performance prediction can be realized for unsupervised learning approaches in the context of video content analysis and indexing. Performance prediction helps in identifying the number of detection errors and can thus support post-processing. This is demonstrated for the example of temporal video segmentation by presenting an approach for automatically predicting the precision and recall of a video cut detection result. It is shown for the unsupervised cut detection approach that the related clustering validity measure is highly correlated with the precision of a detection result. Three regression methods are investigated to exploit the observed correlation. Experimental results demonstrate the feasibility of the proposed performance prediction approach.