Accession Number:

AD1037679

Title:

Feature Weighting via Optimal Thresholding for Video Analysis (Open Access)

Descriptive Note:

Conference Paper

Corporate Author:

The University of Queensland Brisbane Australia

Report Date:

2014-03-03

Pagination or Media Count:

8.0

Abstract:

Fusion of multiple features can boost the performance of large-scale visual classification and detection tasks like TRECVID Multimedia Event Detection MED competition 1. In this paper, we propose a novel feature fusion approach, namely Feature Weighting via Optimal Thresholding FWOT to effectively fuse various features. FWOT learns the weights, thresholding and smoothing parameters in a joint framework to combine the decision values obtained from all the individual features and the early fusion. To the best of our knowledge, this is the first work to consider the weight and threshold factors of fusion problem simultaneously. Compared to state-of-the-art fusion algorithms, our approach achieves promising improvements on HMDB 8 action recognition dataset and CCV 5 video classification dataset. In addition, experiments on two TRECVID MED 2011 collections show that our approach outperforms the state-of-the-art fusion methods for complex event detection.

Subject Categories:

  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE