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inria-00548595, version 1

Dataset issues in object recognition

Jean Ponce () ¹, Tamara Berg ², Mark Everingham ³, David Forsyth ⁴, Martial Hebert ⁵, Svetlana Lazebnik ¹, Marcin Marszałek ⁶, Cordelia Schmid ( Author to contact preferably ) ⁶, Bryan Russell ⁷, Antonio Torralba ⁷, Chris Williams ⁸, Jianguo Zhang ⁶, Andrew Zisserman ()^a^, 3

Towards Category-Level Object Recognition Springer (Ed.) (2006) 29--48

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.

a – University of Oxford
1: The Beckman Institute for Advanced Science and Technology (Beckman Institute)
University of Illinois
2: Computer Science Division [Berkeley]
University of California, Berkeley
3: Visual Geometry Group (VGG)
University of Oxford
4: Department of Computer Science [UIUC] (UIUC)
University of Illinois at Urbana-Champaign
5: The Robotics Institute
Carnegie Mellon University
6: LEAR (IMAG-INRIA Rhône-Alpes / GRAVIR)
CNRS : FR71 – CNRS : UMR5527 – INRIA – Université Joseph Fourier - Grenoble I – Institut National Polytechnique de Grenoble (INPG)
7: Computer Science and Artificial Intelligence Laboratory (CSAIL)
Massachussetts Institute of Technology (MIT)
8: School of Informatics (Informatics)
University of Edinburgh

inria-00548595, version 1
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Submitted for: Laure BENAC <>
Submitted on: Monday, 20 December 2010 09:49:46
Updated on: Thursday, 6 January 2011 10:21:50

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Dataset issues in object recognition

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