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Spatial-Taxon Information Granules as Used in Iterative Fuzzy-Decision-Making for Image Segmentation

  • Chapter
Granular Computing and Decision-Making

Part of the book series: Studies in Big Data ((SBD,volume 10))

Abstract

An image conveys multiple meanings depending on the viewing context and the level of granularity at which the viewer perceptually organizes the scene. In image processing, an image can be similarly organized by means of a standardized natural-scene-taxonomy, borrowed from the study of human visual taxometrics. Such a method yields a three-dimensional representation comprised of a hierarchy of nested spatial-taxons. Spatial-taxons are information granules composed of pixel regions that are stationed at abstraction levels within hierarchically-nested scene-architecture. They are similar to the Gestalt psychological designation of figure-ground, but are extended to include foreground, object groups, objects and salient object parts. By using user interaction to determine scene scale and taxonomy structure, image segmentation can be operationalized into a series of iterative two-class fuzzy inferences. Spatial-taxons are segmented from a natural image via a three step process. This chapter provides a gentle introduction to analogous human language and vision information-granules; and decision systems, modeled on fuzzy natural vision-based reasoning, that exploit techniques for measuring human consensus about spatial-taxon structure. A system based on natural vision-based reasoning is highly non-linear and dynamical. It arrives at an end-point spatial-taxon by adjusting to human input as it iterates. Human input determines the granularity of the query and consensus regarding spatial-taxon regions. The methods of concept algebra developed for computing with words [42] [48] are applied to spatial-taxons. Tools from the study of chaotic systems, such as tools for avoiding iteration problems, are explained in the context of fuzzy inference.

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Authors and Affiliations

  1. Berkeley Initiative for Soft Computing (BISC), University of California, Berkeley, USA

    Lauren Barghout

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  1. Lauren Barghout
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Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Alberta, Canada

    Witold Pedrycz

  2. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

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Barghout, L. (2015). Spatial-Taxon Information Granules as Used in Iterative Fuzzy-Decision-Making for Image Segmentation. In: Pedrycz, W., Chen, SM. (eds) Granular Computing and Decision-Making. Studies in Big Data, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-319-16829-6_12

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  • DOI: https://doi.org/10.1007/978-3-319-16829-6_12

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16828-9

  • Online ISBN: 978-3-319-16829-6

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