NDLI: Local inter-session variability modelling for object classification

Content Provider	IEEE Xplore Digital Library
Author	Anantharajah, K. ZongYuan Ge McCool, C. Denman, S. Fookes, C. Corke, P. Tjondronegoro, D. Sridharan, S.
Copyright Year	2014
Description	Author affiliation: SAIVT & MILAB, QUT, Brisbane, QLD, Australia (Anantharajah, K.) \|\| CyPhy Lab., QUT, Brisbane, QLD, Australia (ZongYuan Ge; Corke, P.) \|\| NICTA, Brisbane, QLD, Australia (McCool, C.) \|\| SAIVT, QUT, Brisbane, QLD, Australia (Denman, S.; Fookes, C.; Sridharan, S.) \|\| MILAB, QUT, Brisbane, QLD, Australia (Tjondronegoro, D.)
Abstract	Object classification is plagued by the issue of session variation. Session variation describes any variation that makes one instance of an object look different to another, for instance due to pose or illumination variation. Recent work in the challenging task of face verification has shown that session variability modelling provides a mechanism to overcome some of these limitations. However, for computer vision purposes, it has only been applied in the limited setting of face verification. In this paper we propose a local region based intersession variability (ISV) modelling approach, and apply it to challenging real-world data. We propose a region based session variability modelling approach so that local session variations can be modelled, termed Local ISV. We then demonstrate the efficacy of this technique on a challenging real-world fish image database which includes images taken underwater, providing significant real-world session variations. This Local ISV approach provides a relative performance improvement of, on average, 23% on the challenging MOBIO, Multi-PIE and SCface face databases. It also provides a relative performance improvement of 35% on our challenging fish image dataset.
Sponsorship	IEEE Comput. Soc.
Starting Page	309
Ending Page	316
File Size	1227464
Page Count	8
File Format	PDF
ISBN	9781479949854
DOI	10.1109/WACV.2014.6836084
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-03-24
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face Adaptation models Databases Lighting Protocols Covariance matrices Face recognition
Content Type	Text

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