Similarity-based perceptual reasoning for perceptual computing

  • Authors:

  • Dongrui Wu;Jerry M. Mendel

  • Affiliations:

  • Signal and Image Processing Institute, Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA;Signal and Image Processing Institute, Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA

  • Venue:
  • FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
  • Year:
  • 2009

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Abstract

Perceptual reasoning (PR) is an approximate reasoning method that can be used as a computing with words (CWW) engine in perceptual computing. There can be different approaches to implement PR, e.g., PR using firing intervals is proposed in [8], [9], [16], and similarity-based PR is proposed in this paper. Both approaches satisfy the constraint on a CWW engine, i.e., the result of combining fired rules should lead to a footprint of uncertainty (FOU) that resembles the three kinds of FOUs in a CWW codebook. A comparative study shows that the output FOUs from similarity-based PR more closely resemble the three kinds of FOUs in a codebook, and the resulting linguistic descriptions are more intuitive; so, similarity-based PR is a better choice for a CWW engine.