Computing color categories

S.N. Yendrikhovskij

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    10 Citations (Scopus)

    Abstract

    This paper is an attempt to develop a coherent framework for understanding, modeling, and computing color categories. The main assumption is that the structure of color category systems originates from the statistical structure of the perceived color environment. This environment can be modeled as color statistics of natural images in some perceptual and approximately uniform color space (e.g., the CIELUV color space). The process of color categorization can be modeled as the grouping of the color statistics by clustering algorithms (e.g., K-means). The proposed computational model enable to predict the location, order, and number of color categories. The model is examined on the basis of K-means clustering analysis of statistics of 630 natural images in the CIELUV color space. In general, the predictions are consistent with Berlin and Kai, and Boynton and Oslon2 data.
    Original languageEnglish
    Title of host publicationHuman Vision and Electronic Imaging V, January 24-27, 2000, San Jose, USA
    EditorsB.E. Rogowitz, T.N. Pappas
    Place of PublicationBellingham
    PublisherSPIE
    Pages356-364
    DOIs
    Publication statusPublished - 2000
    Eventconference; Human vision and Electronic Imaging V; 2000-01-24; 2000-01-27 -
    Duration: 24 Jan 200027 Jan 2000

    Publication series

    NameProceedings of SPIE
    Volume3959
    ISSN (Print)0277-786X

    Conference

    Conferenceconference; Human vision and Electronic Imaging V; 2000-01-24; 2000-01-27
    Period24/01/0027/01/00
    OtherHuman vision and Electronic Imaging V

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  • Cite this

    Yendrikhovskij, S. N. (2000). Computing color categories. In B. E. Rogowitz, & T. N. Pappas (Eds.), Human Vision and Electronic Imaging V, January 24-27, 2000, San Jose, USA (pp. 356-364). (Proceedings of SPIE; Vol. 3959). SPIE. https://doi.org/10.1117/12.387172