Histogram-based automatic segmentation of images

The segmentation process is defined by separating the objects as clustering in the images. The most used method in the segmentation is k-means clustering algorithm. k-means clustering algorithm needs the number of clusters, the initial central points of clusters as well as the image information. However, there is no preliminary information about the number of clusters in real-life problems. The parameters defined by the user in the segmentation algorithms affect the results of segmentation process. In this study, a general approach performing segmentation without requiring any parameters has been developed. The optimum cluster number has been obtained searching the histogram both vertically and horizontally and recording the local and global maximum values. The quite nearly values have been omitted, since the near local peaks are nearly the same objects. Segmentation processes have been performed with k-means clustering giving the possible centroids of the clusters and the optimum cluster number obtained from the histogram. Finally, thanks to histogram method, the number of clusters of k-means clustering has been automatically found for each image dataset. And also, the histogram-based finding of the number of clusters in datasets could be used prior to clustering algorithm for other signal or image-based datasets. These results have shown that the proposed hybrid method based on histogram and k-means clustering method has obtained very promising results in the image segmentation problems.