Double fusion for multimedia event detection

Abstract

Multimedia Event Detection is a multimedia retrieval task with the goal of finding videos of a particular event in an internet video archive, given example videos and descriptions. We focus here on mining features of example videos to learn the most characteristic features, which requires a combination of multiple complementary types of features. Generally, early fusion and late fusion are two popular combination strategies. The former one fuses features before performing classification and the latter one combines output of classifiers from different features. In this paper, we introduce a fusion scheme named double fusion, which combines early fusion and late fusion together to incorporate their advantages. Results are reported on TRECVID MED 2010 and 2011 data sets. For MED 2010, we get a mean minimal normalized detection cost (MNDC) of 0.49, which exceeds the state of the art performance by more than 12 percent. © 2012 Springer-Verlag.