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Modeling of subjective interpretation for street landscape image

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Database and Expert Systems Applications (DEXA 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1460))

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Abstract

Our hypothesis is that the model of subjective interpretation is based on rough information process. We analyze a relationship between adjectives and three graphical attributes of color, direction, and position in order to model the subjective interpretation for streetscape images. We focus especially on the graphical descriptors such as Lab color space, Lab auto-correlation and rough image segmentation, which correspond to the graphical attributes. Our psychological experiment indicates that subjective interpretations consist partly of “evaluation”, “texture”, and “openness”. By analyzing the relationships between these three factors and the graphical descriptors, we find that subjective interpretation relates to the combination of the three graphical descriptors.

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Author information

Authors and Affiliations

  1. Electrotechnical Laboratory, 1-1-4 Umezono, Tsukuba, 305-8568, Ibaraki, Japan

    Tatsuya Shibata & Toshikazu Kato

  2. Japan Science and Technology Corporation, 4-1-8 Honmachi, Kawaguchi, 332-0012, Saitama, Japan

    Tatsuya Shibata

  3. Dept. of Industrial and Systems Engineering, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, 112-0003, Tokyo, Japan

    Toshikazu Kato

Authors
  1. Tatsuya Shibata
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  2. Toshikazu Kato
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Editor information

Gerald Quirchmayr Erich Schweighofer Trevor J.M. Bench-Capon

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© 1998 Springer-Verlag Berlin Heidelberg

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Shibata, T., Kato, T. (1998). Modeling of subjective interpretation for street landscape image. In: Quirchmayr, G., Schweighofer, E., Bench-Capon, T.J. (eds) Database and Expert Systems Applications. DEXA 1998. Lecture Notes in Computer Science, vol 1460. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0054508

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  • DOI: https://doi.org/10.1007/BFb0054508

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64950-2

  • Online ISBN: 978-3-540-68060-4

  • eBook Packages: Springer Book Archive

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