Non-Orthogonal Binary Subspace and Its Applications in Computer Vision

Authors:
Hai Tao;Ryan Crabb;Feng Tang
Affiliations:
University of California at Santa Cruz;University of California at Santa Cruz;University of California at Santa Cruz
Venue:
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Year:
2005

Citing 0
Cited 2

Fast linear discriminant analysis using binary bases

Pattern Recognition Letters
Representing Images Using Nonorthogonal Haar-Like Bases

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a novel approach that represents an image or a set of images using a non-orthogonal binary subspace (NBS) spanned by box-like base vectors. These base vectors possess the property that the inner product operation with them can be computed very efficiently. We investigate the optimized orthogonal matching pursuit method for finding the best NBS base vectors. It is demonstrated in this paper how the NBS based expansion can be applied to speed up several common computer vision algorithms, including normalized cross correlation (NCC), sum of squared difference (SSD) matching, appearance subspace projection and subspace-based object recognition. Promising experimental results on facial and natural images are demonstrated in this paper.