Separable linear classifiers for online learning in appearance based object detection

Authors:
Christian Bauckhage;John K. Tsotsos
Affiliations:
Centre for Vision Research, York University, Toronto, ON;Centre for Vision Research, York University, Toronto, ON
Venue:
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
Year:
2005

Citing 6
Cited 0

EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation

International Journal of Computer Vision
Image Processing Done Right

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Learning to Detect Objects in Images via a Sparse, Part-Based Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
An unsupervised, online learning framework for moving object detection

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A cognitive vision system for action recognition in office environments

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Optimal parallel 2-D FIR digital filter with separable terms

IEEE Transactions on Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Online learning for object detection is an important requirement for many computer vision applications. In this paper, we present an iterative optimization algorithm that learns separable linear classifiers from a sample of positive and negative example images. We demonstrate that separability not only leads to rapid runtime behavior but enables very fast training. Experimental results underline that the approach even allows for real time online learning for tracking of articulated objects in real world environments.