Activity Recognition from Sparsely Labeled Data Using Multi-Instance Learning

Abstract. Activity recognition has attracted increasing attention in recent years due to its potential to enable a number of compelling contextaware applications. As most approaches rely on supervised learning methods, obtaining substantial amounts of labeled data is often an important bottle-neck for these approaches. In this paper, we present and explore a novel method for activity recognition from sparsely labeled data. The method is based on multi-instance learning allowing to significantly reduce the required level of supervision. In particular we propose several novel extensions of multi-instance learning to support different annotation strategies. The validity of the approach is demonstrated on two public datasets for three different labeling scenarios.

• Type: Conference
• Year: 2009
• Where: LOCA
• Authors: Maja Stikic, Bernt Schiele
• Keywords: Activity Recognition, LOCA 2009, Multi-instance Learning, Supervised Learning Methods