Digital Image Clustering Algorithm based on Multi-agent Center Optimization
PAN Xin, H. Sagan
Journal of Digital Information Management, Vol. 14, No. 1, 2016.
DOI: 
Link: http://dline.info/fpaper/jdim/v14i1/v14i1_2.pdf
Abstract:
Digital image clustering algorithms can
classify pixels according to their data characteristics
without the pre-input of training samples. The number
of categories and center point value are difficult to determine
because of the large size of pixels and several
features of digital image data. This paper proposed a
digital image clustering algorithm based on multi-agent
center optimization (DICA-MCO). The proposed algorithm
established a problem optimization and solving
system composed of agents. To achieve fuzzy evaluation,
DICA-MCO mapped the digital image clustering
problem as a problem of intelligent agent movement in
a multidimensional solution space. Results demonstrat
that compared with traditional algorithms, DICA-MCO
can select the optimal number of categories and value
of center points and has high classification accuracy,
Kappa coefficient, and classification effect.