Outdoor scene image segmentgation using statistical region merging

A new loom of outdoor scene image segmentation algorithm is based on the region amalgamation. Here we are going to identify both structured (e.g. buildings, persons, car, etc.) and unstructured background objects (sky, road, grass, etc.) which are containing the some characteristic based on color, intensity, and texture in sequence. Our main aim is to solve the over segmented objects and strong reflection of objects. These problems are solved by using SRM (Statistical Region Merging) algorithm. In pre-processing the input image is converted into CIE (Commission Internationalde Eclairage) color space technique. Then bottom-up segmentation process is used to capture the structured and unstructured image characteristics. Another process is the Ada boost classifier which is used to classify the background objects in outdoor environment scenes. Ada boost is focused on difficult patterns. Then the contour maps are used to detect the boundary energy. Boundary detection test is the grouping of objects with a pair of connected neighboring regions. In this paper we have used an experimental result of two databases (Gould data set and Berkeley segmentation data set) and provide accurate segmentation using region merging. Finally the statistical region merging provides the groupings of images to identify the computer vision.