Local Inter-Session Variability Modelling for Object Classification

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Anantharajah, Kaneswaran
Ge, ZongYuan
McCool, Chris
Denman, Simon
Fookes, Clinton
Corke, Peter
Tjondronegoro, Dian
Sridharan, Sridha
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Steamboat Springs, CO, USA


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.

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IEEE Winter Conference on Applications of Computer Vision

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Artificial Intelligence and Image Processing

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Computer Science, Artificial Intelligence

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Anantharajah, K; Ge, Z; McCool, C; Denman, S; Fookes, C; Corke, P; Tjondronegoro, D; Sridharan, S, Local Inter-Session Variability Modelling for Object Classification, IEEE Winter Conference on Applications of Computer Vision, 2014, pp. 309-316