TY - GEN
T1 - A sensation model for color images' cognition
AU - Li, Zhong Sheng
AU - Huang, Tong Cheng
AU - Niu, Li
AU - Cai, Ze Su
PY - 2013
Y1 - 2013
N2 - 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".
AB - 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".
KW - Data processing task
KW - Rough set
KW - Semantic partitioning
KW - Single concept clustering(SCC)
UR - https://www.scopus.com/pages/publications/84874685524
U2 - 10.4028/www.scientific.net/AMM.303-306.1489
DO - 10.4028/www.scientific.net/AMM.303-306.1489
M3 - 会议稿件
AN - SCOPUS:84874685524
SN - 9783037856529
T3 - Applied Mechanics and Materials
SP - 1489
EP - 1493
BT - Sensors, Measurement and Intelligent Materials
T2 - 2012 International Conference on Sensors, Measurement and Intelligent Materials, ICSMIM 2012
Y2 - 26 December 2012 through 27 December 2012
ER -