A Sensation Model for Color Images’ Cognition

Abstract:

It’s a new idea to make computers be able to obtain “sensations” from a color image through some unsupervised ways. To let the idea come into true, a granule-based model, based on granular computing(GrC) which is a new way to simulate human thinking to help solve complicated problems in the field of computational intelligence, is proposed for color image processing. First, this paper deems data a hypercube, defines two new concepts, attribute granules(AtG) and connected granules(CoG), and presents the definitions of the granule-based model. Then, in order to fulfill the granule-based model, this paper designs a single attribute analyser(SAA), defines some theorems and lemmas related to decomposition, and describes the processing of extracting all attibute granules. Experimental results on over 300 color images show that the proposed analyser is accurate, robust, high-speed, and able to provide computers with “sensations”.

• Periodical: Applied Mechanics and Materials (Volumes 303-306)
• Pages: 1489-1493
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.303-306.1489
• Online since: February 2013
• Authors: Zhong Sheng Li, Tong Cheng Huang, Niu Li, Ze Su Cai
• Keywords: Data Processing Task, Rough Set (RS), Semantic Partitioning, Single Concept Clustering (SCC)

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