The 2005 PASCAL Visual Object Classes Challenge

Laaksonen, J; Larlus, D; Schiele, B; Everingham, M; Zisserman, A; Williams, CKI; van Gool, L; Allan, M; Bishop, CM; Chapelle, O; Dalal, N; Deselaers, T; Dorkó, G; Duffner, S; Eichhorn, J; Fritz, M; Garcia, C; Griffiths, T; Jurie, F; Keysers, T; Koskela, M; Leibe, B; Meng, H; Ney, H; Schmid, C; Seemann, E; Shawe‐Taylor, J; Storkey, A; Szedmak, S; Triggs, B; Ulusoy, I; Viitaniemi, V; Jianguo, Z

doi:10.1007/11736790_8

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The 2005 PASCAL Visual Object Classes Challenge

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Chapelle, O
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Eichhorn, J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Laaksonen, J., Larlus, D., Schiele, B., Everingham, M., Zisserman, A., Williams, C., et al. (2005). The 2005 PASCAL Visual Object Classes Challenge. In J. Quiñonero-Candela, I. Dagan, B. Magnini, & F. d’Alché-Buc (Eds.), Machine Learning Challenges: Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment First PASCAL Machine Learning Challenges Workshop, MLCW 2005, Southampton, UK, April 11-13, 2005 (pp. 117-176). Berlin: Springer.

Cite as: https://hdl.handle.net/21.11116/0000-0005-38D4-D

Abstract

The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). Four object classes were selected: motorbikes, bicycles, cars and people. Twelve teams entered the challenge. In this chapter we provide details of the datasets, algorithms used by the teams, evaluation criteria, and results achieved.