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 proceedingChapter

Abstract / Description of output

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 languageEnglish
Title of host publicationToward Category-Level Object Recognition
EditorsJean Ponce, Martial Hebert, Cordelia Schmid, Andrew Zisserman
PublisherSpringer Berlin Heidelberg
Pages29-48
Number of pages20
DOIs
Publication statusPublished - 2006

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
Volume4170
ISSN (Print)0302-9743

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