A saliency-based approach to event recognition

Over the last few years, a number of interesting solutions covering different aspects of event recognition have been proposed for event-based multimedia analysis. Existing approaches mostly focus on an efficient representation of the image and advanced classification schemes. However, it would be desirable to focus on the event-specific information available in the image, namely the so-called event saliency. In this paper, we propose a novel approach based on multiple instance learning (MIL) to learn the visual features contained in event-salient regions, extracted through a crowd-sourcing study. In total, we collect the salient regions for 76 different events from 4 large-scale datasets. The experimental results demonstrate the efficacy of using only event-related regions by achieving a significant gain in performance over the state-of-the-art.