Adapted Normalized Graph Cut Segmentation with Boundary Fuzzy Classifier Object Indexing On Road Satellite Images

Abstract: Image segmentation is an essential component of the remote sensing, image inspection, classification and pattern identification. The road satellite image categorization points a momentous tool for the assessment of images. In the present work, the researchers have evaluated the computer vision techniques for instance segmentation, and knowledge based techniques for categorization of high-resolution descriptions. For sorting of the road satellite images, the technique named Adapted normalized Graph cut Segmentation with Boundary Fuzzy classifier object Indexing (AGSBFI) is introduced. Initially, the road satellite image is segmented to have inverse determination of shapes using adapted normalized graph cut segmentation method. The features of the segmented area are extracted and then classification of unknown boundary is carried out using boundary fuzzy classifier. Finally the classified images are then recognized based on the location using the arbitrary object indexing scheme. Performance of AGSBFI technique is measured in terms of classification efficiency and objects recognition accuracy with better results. AGSBFI considers the problem of inverse determination of unknown shape, boundary and location in the existing method. An analytical and empirical result shows the better object recognition accuracy with inverse determination of shape, boundary and location of road satellite images.

Keywords: Image segmentation, boundary fuzzy classifier, adapted normalized graph cuts, arbitrary object indexing, categorization, road satellite images.

 Received April 8, 2013; accepted  June 25, 2013