Active learning for simultaneous annotation of multiple binary semantic concepts

Abstract

Model-based approach to video analysis requires annotated corpora. Video annotation, however is a very expensive process. Tools that allow users to annotate video shots with scenes, events, and objects should minimize user interaction. These tools should particularly leverage redundancy in content and advances in machine learning and human computer intelligent to reduce the amount of human interaction needed to annotate large corpora. As corpora sizes and the lexicon grows, this is increasingly relevant. Active Learning can play a critical role in reducing the amount of supervision. In this paper we apply active learning to the simultaneous annotation of multiple binary concepts. The challenge is to minimize the total number of samples to be annotated across all concepts. Preliminary experiments with the simultaneous annotation of two concepts Outdoors and Indoors using the TRECVID corpus are promising and reduce annotation workload significantly.