Feature weighting via optimal thresholding for video analysis

Publication Type:
Conference Proceeding
Proceedings of the IEEE International Conference on Computer Vision, 2013, pp. 3440 - 3447
Issue Date:
Filename Description Size
Thumbnail2013004175OK.pdf796.67 kB
Adobe PDF
Full metadata record
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. © 2013 IEEE.
Please use this identifier to cite or link to this item: