Dataset issues in object recognition

J. Ponce; T. L. Berg; M. Everingham; D. A. Forsyth; M. Hebert; S. Lazebnik; M. Marszalek; C. Schmid; B. C. Russell; A. Torralba; C. K. I. Williams; J. Zhang; A. Zisserman

doi:10.1007/11957959_2

Dataset issues in object recognition

J. Ponce, T. L. Berg, M. Everingham, D. A. Forsyth, M. Hebert, S. Lazebnik, M. Marszalek, C. Schmid, B. C. Russell, A. Torralba, C. K. I. Williams, J. Zhang, A. Zisserman

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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.

Original language	English
Title of host publication	Toward category-level object recognition
Editors	Jean Ponce, Martial Hebert, Cordelia Schmid, Andrew Zisserman
Place of Publication	Berlin
Publisher	Springer
Pages	29-48
Number of pages	20
ISBN (Electronic)	9783540687955
ISBN (Print)	9783540687948
DOIs	https://doi.org/10.1007/11957959_2
Publication status	Published - 2006

Publication series

Name	Lecture notes in computer science
Publisher	Springer
Volume	4170
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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10.1007/11957959_2

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Ponce, J., Berg, T. L., Everingham, M., Forsyth, D. A., Hebert, M., Lazebnik, S., Marszalek, M., Schmid, C., Russell, B. C., Torralba, A., Williams, C. K. I., Zhang, J., & Zisserman, A. (2006). Dataset issues in object recognition. In J. Ponce, M. Hebert, C. Schmid, & A. Zisserman (Eds.), Toward category-level object recognition (pp. 29-48). (Lecture notes in computer science; Vol. 4170). Springer . https://doi.org/10.1007/11957959_2

@inbook{3d43eff28e424f2eb915109314465fc9,

title = "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.",

author = "J. Ponce and Berg, {T. L.} and M. Everingham and Forsyth, {D. A.} and M. Hebert and S. Lazebnik and M. Marszalek and C. Schmid and Russell, {B. C.} and A. Torralba and Williams, {C. K. I.} and J. Zhang and A. Zisserman",

note = "This book is the outcome of two workshops that were held in Taormina in 2003 and 2004.",

year = "2006",

doi = "10.1007/11957959_2",

language = "English",

isbn = "9783540687948",

series = "Lecture notes in computer science",

publisher = "Springer ",

pages = "29--48",

editor = "Jean Ponce and Martial Hebert and Cordelia Schmid and Zisserman, {Andrew }",

booktitle = "Toward category-level object recognition",

}

Ponce, J, Berg, TL, Everingham, M, Forsyth, DA, Hebert, M, Lazebnik, S, Marszalek, M, Schmid, C, Russell, BC, Torralba, A, Williams, CKI, Zhang, J & Zisserman, A 2006, Dataset issues in object recognition. in J Ponce, M Hebert, C Schmid & A Zisserman (eds), Toward category-level object recognition. Lecture notes in computer science, vol. 4170, Springer , Berlin, pp. 29-48. https://doi.org/10.1007/11957959_2

TY - CHAP

T1 - Dataset issues in object recognition

AU - Ponce, J.

AU - Berg, T. L.

AU - Everingham, M.

AU - Forsyth, D. A.

AU - Hebert, M.

AU - Lazebnik, S.

AU - Marszalek, M.

AU - Schmid, C.

AU - Russell, B. C.

AU - Torralba, A.

AU - Williams, C. K. I.

AU - Zhang, J.

AU - Zisserman, A.

N1 - This book is the outcome of two workshops that were held in Taormina in 2003 and 2004.

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

U2 - 10.1007/11957959_2

DO - 10.1007/11957959_2

M3 - Chapter

SN - 9783540687948

T3 - Lecture notes in computer science

SP - 29

EP - 48

BT - Toward category-level object recognition

A2 - Ponce, Jean

A2 - Hebert, Martial

A2 - Schmid, Cordelia

A2 - Zisserman, Andrew

PB - Springer

CY - Berlin

ER -

Dataset issues in object recognition

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