Küçükkülahlı, EnverErdoğmuş, PakizePolat, Kemal2020-04-302020-04-302017978-1-5090-6494-62165-0608https://hdl.handle.net/20.500.12684/236025th Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2017 -- Antalya, TURKEYWOS: 000413813100052Image segmentation is the process of separating objects within an image. Image segmentation can be considered as an important computer vision problem in image sensing where the homogeneous regions in an image can be distinguished with high accuracy. In this study, a two stage hybrid method has been proposed for image segmentation. In the first stage, the Histogram Based Cluster Estimation (HBCE) is used to determine the number of clusters on the image. In the second stage, the cluster numbers determined by the HBCE algorithm are given to the BBO (Biogeography based optimization) algorithm and then image segmentation is performed. In this study, the proposed hybrid image segmentation method was applied to 6 different images taken from Berkeley database and compared with human segmentation results obtained from the same database. To test the performance of the proposed image segmentation method, RI (Rand Index), GCE (Global Consistency Error) and run time as comparison criterion have been used. The proposed method has been compared with other hybrid methods namely HBCE-PSO (Particle Swarm Optimization) and HBCE-k means clustering. When running on 6 different images, the best Rand Index values from the results obtained for all three methods are as follows; HBCE-BBO incorporation: 0.9859, HBCE-PSO incorporation: 0.9856, HBCE-k means incorporation: 0.7570. The results have shown that the HBCE-BBO hybrid method yields better results than the other two hybrid methods used in working with 6 different image segmentations.trinfo:eu-repo/semantics/closedAccessclusteringimage segmentationBiogeography based optimizationHistogram Based Cluster EstimationConference ObjectN/A