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

    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
    Place of PublicationBerlin
    PublisherSpringer
    Pages29-48
    Number of pages20
    ISBN (Electronic)9783540687955
    ISBN (Print)9783540687948
    DOIs
    Publication statusPublished - 2006

    Publication series

    NameLecture notes in computer science
    PublisherSpringer
    Volume4170
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

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  • Cite this

    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