Fast Discriminative Linear Models for Scalable Video Tagging

Roberto Paredes, Adrian Ulges, Thomas Breuel
Proceedings of the International Conference on Machine Learning and Applications, Miami, Florida, USA, IEEE, 12/2009

Abstract:

While video tagging (or "concept detection") is a key building block of research prototypes for video retrieval, its practical use is hindered by the computational effort associated with learning and detecting thousands of concepts. Support vector machines (SVMs), which can be considered the standard approach, scale poorly since the number of support vectors is usually high. In this paper, we propose a novel alternative that offers the benefits of rapid training and detection. This linear-discriminative method is based on the maximization of the area under the ROC. In quantitative experiments on a publicly available dataset of web videos, we demonstrate that this approach offers a significant speedup at a moderate performance loss compared to SVMs, and also outperforms another well-known linear-discriminative method based on a Passive-Aggressive Online Learning (PAMIR).

Files:

  PID1025841.pdf

BibTex:

@inproceedings{ PARE2009,
	Title = {Fast Discriminative Linear Models for Scalable Video Tagging},
	Author = {Roberto Paredes and Adrian Ulges and Thomas Breuel},
	BookTitle = {Proceedings of the International Conference on Machine Learning and Applications},
	Month = {12},
	Year = {2009},
	Publisher = {IEEE}
}

     
Last modified:: 30.08.2016