Benchmarking Gradient Magnitude Techniques for Image Segmentation Using CBIR

As image segmentation has become a definite prerequisite in many of the image processing and computer vision applications, an effort towards evaluating such segmentation techniques is indeed found very less in literature. In this paper, we carried out a comprehensive evaluation of five different gradient magnitude (GM) based image segmentation techniques using CBIR (Content Based Image Retrieval). Firstly, boundary probabilities are detected using the gradient magnitude based techniques such as - Canny edge detection (pbCanny), Second moment matrix (pb2MM), Multi-scale second moment matrix (pb2MM2), Gradient magnitude (pbGM) and Multi-scale gradient magnitude (pbGM2). Further, Ridgelets are applied to these boundaries to extract radial energy information exhibiting linear properties and PCA to reduce the dimensionality of these features. Finally, probabilistic neural network (PNN) classifiers are used to classify and observe the performance of gradient magnitude techniques in classification process. We observed the performance of these algorithms on the most challenging and popular image datasets namely Corel-1K, Caltech-101, and Caltech-256.