Gradual color clustering elimination for outdoor image segmentation

Abstract

One of the color reduction methods is color clustering, which has been applied for segmentation. Nonetheless, it has not been an appropriate method due to the automatically images change by luminance effects and color/texture variety. Hence, it can be done by improving the usual color clustering methods called customizing segmentation methods. This study focuses on customizing the color clustering methods for segmentation and object recognition in the outdoor images by utilizing a multi - phase procedure through a multi - resolution platform, based on self - organizing neural network, call ed gradual color Cluster Elimination (GCCE). The proposed method has been evaluated on outdoor images dataset namely BSDS and the results have been compared to PRI, NPR, and GCE statistical metrics of the latest segmentation methods which demonstrated that the proposed method has a satisfactory performance for the segmentation of the outdoor scenes.