Underwater Image Transition Region Extraction and Segmentation Based on SVM

Abstract

By analyzing the gray level features of transition region, a new underwater image transition region extraction method based on Support Vector Machine (SVM) is presented. At first, a vector is constructed to fully describe the transition region, which includes local complexity, local difference and neighborhood homogeneity. Then, SVM is applied to train and classify the set of feature vectors, so that the transition region of the underwater image is extracted. Finally, the segmentation threshold is determined by mean of the histogram of the transition region, and the binary result was yielded. The experimental results show that the proposed algorithm can achieve a better transition region extraction and segmentation performance, and automatically select the optimal threshold for transition region extraction.