Small Target Detection combining Foreground and Background Manifolds

Sebastien Razakarivony 1 Frédéric Jurie 2
1 Image
SAGEM Défense Sécurité [Massy]
2 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : This paper focuses on the detection of small objects (e.g. vehicules in aerial images) on complex back-grounds (e.g. natural backgrounds). A key contribution of the paper is to show that, in such situations, learning a target model and a background model separately is better than training a unique discriminative model. This contrasts with standard object detection approaches for which objects vs. background classi fiers use the same types of visual features/models for both. The second contribution lies in the use of manifold learning approaches to build these models. The proposed detection algorithm is validated on the publicly available OIRDS dataset, on which we obtain state-of-the-art results.
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Sebastien Razakarivony, Frédéric Jurie. Small Target Detection combining Foreground and Background Manifolds. IAPR International Conference on Machine Vision Applications, May 2013, Kyoto, Japan. 4 pp. ⟨hal-00943444⟩

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