Mimicking human texture classification

B.E. Rogowitz (Editor), Eva M. van Rikxoort, Egon van den Broek, T.N. Pappas (Editor), Theo E. Schouten, S.J. Daly (Editor)

    Research output: Contribution to conferencePaperpeer-review

    6 Citations (Scopus)
    46 Downloads (Pure)


    In an attempt to mimic human (colorful) texture classification by a clustering algorithm three lines of research have been encountered, in which as test set 180 texture images (both their color and gray-scale equivalent) were drawn from the OuTex and VisTex databases. First, a k-means algorithm was applied with three feature vectors, based on color/gray values, four texture features, and their combination. Second, 18 participants clustered the images using a newly developed card sorting program. The mutual agreement between the participants was 57% and 56% and between the algorithm and the participants it was 47% and 45%, for respectively color and gray-scale texture images. Third, in a benchmark, 30 participants judged the algorithms’ clusters with gray-scale textures as more homogeneous then those with colored textures. However, a high interpersonal variability was present for both the color and the gray-scale clusters. So, despite the promising results, it is questionable whether average human texture classification can be mimicked (if it exists at all).
    Original languageUndefined
    Number of pages12
    Publication statusPublished - 18 Mar 2005
    EventHuman Vision and Electronic Imaging X - San Jose, CA, USA
    Duration: 16 Jan 200520 Jan 2005


    ConferenceHuman Vision and Electronic Imaging X
    Other16-20 January 2005


    • Texture
    • card sorting
    • k-Means
    • mimic
    • EWI-21116
    • Color
    • HMI-HF: Human Factors
    • IR-59827
    • Human texture perception
    • Clustering
    • HMI-CI: Computational Intelligence

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