A Hybrid Agglomerative Method for Colour Image Segmentation

Abstract

AbstractThis paper proposes a hybrid method of image segmentation using K-means and agglomerative methods of image segmentation. The K-means method is used to find optimum number of clusters with the help of gap or elbow method and a validity measure. Then this optimum value is used as a limiting value in agglomerative algorithm. The performance of algorithm is measured using a validity index which is measured by two factors. The first factor is intra-cluster distance whose minimum value is desired, and another is inter-cluster distance for which a maximum value is required. Once optimum number of clusters is found then K-means clustering algorithm is again applied to generate large number of clusters, following which pair of clusters with most similar characteristics are merged iteratively until number of clusters are reduced up to optimum number of clusters as decided earlier. The similarity measure is derived from Davies-Bouldin Index. The proposed algorithm is performing better than simple K-means algorithm.KeywordsAgglomerative methodImage processingSegmentationK-meansDavies-bouldin indexValidity indexSimilarityClustering