Dataset issues in object recognition

Abstract : Appropriate datasets are required at all stages of object recognition research, including learning visual models of object and scene categories, detecting and localizing instances of these models in images, and evaluating the performance of recognition algorithms. Current datasets are lacking in several respects, and this paper discusses some of the lessons learned from existing efforts, as well as innovative ways to obtain very large and diverse annotated datasets. It also suggests a few criteria for gathering future datasets.
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Chapitre d'ouvrage
Jean Ponce and Martial Hebert and Cordelia Schmid and Andrew Zisserman. Towards Category-Level Object Recognition, 4170, Springer, pp.29--48, 2006, Lecture Notes in Computer Science (LNCS), 978-3-540-68794-8
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https://hal.inria.fr/inria-00548595
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Soumis le : lundi 20 décembre 2010 - 09:49:46
Dernière modification le : mercredi 11 avril 2018 - 01:54:24

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  • HAL Id : inria-00548595, version 1

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Jean Ponce, Tamara Berg, Mark Everingham, David Forsyth, Martial Hebert, et al.. Dataset issues in object recognition. Jean Ponce and Martial Hebert and Cordelia Schmid and Andrew Zisserman. Towards Category-Level Object Recognition, 4170, Springer, pp.29--48, 2006, Lecture Notes in Computer Science (LNCS), 978-3-540-68794-8. 〈inria-00548595〉

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