实验室教师发表EI论文一篇，发表于： 2016年4月 Journal of Digital Information Management

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). This algorithm establishes a problem optimization and solving system composed of agents. To achieve fuzzy evaluation, DICA-MCO maps the digital image clustering problem as a problem of intelligent agent movement in a multidimensional solution space. Results demonstrated 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.