Semantic image segmentation using oriented pattern analysis

Semantic image segmentation is often served as an instrumental front-end preprocessing of many image processing and computer vision applications. Different from the existing semantic segmentation methods which mostly rely on database training, a simple and effective approach is proposed in this paper by employing the oriented pattern as a class-discriminative feature, besides the use of color and texture. The developed algorithm is to discriminate three specific semantic classes - sky, foliage, and building, which are commonly encountered in typical color images containing outdoor scene. In our approach, two maps will be generated from the input image: 1) the over-segmented map by using JSEG, and 2) the oriented pattern map by measuring the orientation coherence at each pixel position. Based on these maps, the region coherence of each over-segmented patch can be obtained by taking the average of all the orientation coherence values within the patch. The semantic class labeling for the patch can be simply conducted by checking the computed region coherence value against two empirically determined thresholds. Extensive simulation experiments have been conducted by testing various color images acquired from various sources, including the Microsoft Research Cambridge (MSRC) Object Recognition Image Database that contains manually-segmented regions as the ground truth. All experimental results clearly show that the proposed method is able to consistently deliver highly attractive performance.