Galilean-Diagonalized Spatio-Temporal Interest Operators

Authors:
Tony Lindeberg;Amir Akbarzadeh;Ivan Laptev
Affiliations:
Computational Vision and Active Perception Laboratory (CVAP), Sweden;Computational Vision and Active Perception Laboratory (CVAP), Sweden;Computational Vision and Active Perception Laboratory (CVAP), Sweden
Venue:
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Year:
2004

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Abstract

This paper presents a set of image operators for detecting regions in space-time where interesting events occur. To define such regions of interest, we compute a spatio-temporal second-moment matrix from a spatio-temporal scale-space representation, and diagonalize this matrix locally, using a local Galilean transformation in space-time, optionally combined with a spatial rotation, so as to make the Galilean invariant degrees of freedom explicit. From the Galilean-diagonalized descriptor so obtained, we then formulate different types of space-time interest operators, and illustrate their properties on different types of image sequences.