A Binary Morphology-Based Clustering Algorithm Directed by Genetic Algorithm

Mathematical morphology is a formalism largely used in image processing for implementing many different tasks. Several operators that support the formalism have also been successfully used for inducing data clusters. Particularly, the Binary Morphology Clustering Algorithm (BMCA) is one of such inductive methods which, given a set of input patterns and morphological operators, produces clusters of patterns as output. BMCA results, however, are dependent on suitable user-defined values for the set of parameters the algorithm employs namely, the resolution of its initial discretization process, the threshold associated with a distance metric, the threshold associated with region density and the structuring element embedded in morphological operators. This paper proposes a combined approach where an evolutionary algorithm is employed for searching suitable parameter values for BMCA aiming at producing more efficient results as far as the clustering process is concerned. The proposal was implemented as the system BMCAbyGA, used in several successful clustering experiments described in the final part of the paper. BMCAbyGA has been applied to a Cartesian Genetic Programming approach for the automatic construction of image Alters in hardware.