Event Detection using Multi-Level Relevance Labels and Multiple Features

Zhongwen Xu, Ivor W. Tsang, Yi Yang, Zhigang Ma, Alexander G. Hauptmann; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 97-104

Abstract


We address the challenging problem of utilizing related exemplars for complex event detection while multiple features are available. Related exemplars share certain positive elements of the event, but have no uniform pattern due to the huge variance of relevance levels among different related exemplars. None of the existing multiple feature fusion methods can deal with the related exemplars. In this paper, we propose an algorithm which adaptively utilizes the related exemplars by cross-feature learning. Ordinal labels are used to represent the multiple relevance levels of the related videos. Label candidates of related exemplars are generated by exploring the possible relevance levels of each related exemplar via a cross-feature voting strategy. Maximum margin criterion is then applied in our framework to discriminate the positive and negative exemplars, as well as the related exemplars from different relevance levels. We test our algorithm using the large scale TRECVID 2011 dataset and it gains promising performance.

Related Material


[pdf]
[bibtex]
@InProceedings{Xu_2014_CVPR,
author = {Xu, Zhongwen and Tsang, Ivor W. and Yang, Yi and Ma, Zhigang and Hauptmann, Alexander G.},
title = {Event Detection using Multi-Level Relevance Labels and Multiple Features},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2014}
}