Modeling Concept Dependencies for Event Detection
Ethem Fatih Can, Raghaven Manmatha · 2014
Event detection is a recent and challenging task. The aim is to retrieve the relevant videos given an event description. A set of training examples associated with the events are generally provided as well, since retrieving relevant videos from textual queries solely is not feasible. Early attempts of event detection are based on low-level features. High level features such as concepts for event detection have been introduced as an alternative to low-level features since high-level features provide semantically richer information. In this work, we focus on object-based concepts and exploit their dependencies using a Markov Random Field (MRF) based model for event detection. This enables us to model likelihood of concepts, either pairwise or individually, present in the videos. Here, we propose a method incorporating the strengths of concepts and MRF based model for event detection task. We evaluate our models on an Multimedia Event Detection (MED) dataset from NIST's 2011 TRECVID Multimedia, which consists of approximately 45,000 unconstrained videos. This type of work is beneficial from several respects. First, we focus on the task of concept-based event detection using a very large number of unconstrained Youtube videos. Second, we introduce the application of MRF's for the event detection purpose, which can further be enhanced incorporating other features or temporal information. At last but not means least, we exploit the occurrence and co-occurrence of object based concepts for event detection that enables us to reveal interactions of such concepts in the video level. Experimental results show that revealing these interactions provide promising event detection results.