A Powerful and Efficient Method of Image Segmentation Based on Random Forest Algorithm

Segmentation is the heart of an automatic image analysis system. There are several segmentation techniques. The contour segmentation approach aims to separate regions of different gray levels and relatively homogeneous. The region segmentation approach consists in grouping the adjacent pixels of the image into distinct regions. The cooperative approach is used in order to improve the result of segmentation and the segmentation based on classification methods; in this case, classes are defined by the maximum sets of related pixels belonging to the same class. In this work by studying the dual problem, we develop a simple but efficient cooperative approach between a Random forest classification method and a set of contour detection methods as Canny, Prewitt and Sobel. Firstly, original image is initially segmented by hybridization of Canny, Prewitt, and Sobel’s algorithms for edge detection. Then, we will use the output image obtained by another supervised classification segmentation process, which is random forest. To compare between the results obtained by the different methods, we used a several evaluation metrics such us: entropy, MI, IoU and DSC. The advantage and robustness of the proposed method is demonstrated by in-depth experimentation on a set of images.