Feature Weighting via Optimal Thresholding for Video Analysis (Open Access)
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 03, 2014
- Accession Number
- AD1037679
Entities
People
- Alexander G. Hauptmann
- Ivor Tsang
- Nicu Sebe
- Yi Yang
- Zhongwen Xu
Organizations
- University of Queensland