Color image segmentation using genetic algorithm with aggregation-based clustering validity index (CVI)

Clustering validity index (CVI) plays an important role in data partitioning and image segmentation. In this paper, a new CVI is proposed to perform the color image segmentation. The proposed CVI combines compactness, separation and overlap to assess the clustering quality effectively. The aggregation operators (t-norms and t-conorms) are used to build a new reliable and robust overlap measure. Moreover, a genetic algorithm is employed to dynamically optimize the clusters centroids and get the best possible data partition. The clustering of super-pixels is performed to reduce the computational cost and convergence time. The genetic algorithm with new clustering validity index is able to find the best data partitioning. The performance of the proposed algorithm is evaluated on the Berkeley image segmentation database. The extensive experimentation shows that the proposed algorithm performs better compared to other state-of-the-art methods.