Superpixel Dual Extension to Identify Effective Regions for Segmentation-Based Computer Vision Problems

One of the effective methods for visual recognition (including classification, object recognition and image semantic labeling) is to identify probable regions including an object that is known as object proposals. In this paper, an effective approach is proposed relying on identifying appropriate regions based on image segmentation which is called SDE (superpixel dual extension). The proposed approach comprises of two phases. In the first phase, using a bottom-up segmentation algorithm, an image is presented by some regions as superpixels.  In the second phase each superpixel is then extended to adjacent regions, according to a set of predefined states and the 8-connectivity. The most important advantage of this extension is to generate regions that are able to completely surround an object. Using descriptors such as color, texture and keypoints for feature extraction resolves computer vision problems and enhances the performance. Here, a set of well-known metrics of image segmentation including overlap, recall, area under curve, and pair of pixels’ coherency are measured in order to precisely assess the proposed method. Furthermore, to more evaluate the effectiveness of the method a classification problem on MSRC dataset is carried out. The results are shown quality enhancement around 7% for graph-based segmentation and 14% for clustering-based segmentation. Moreover, 11% improvement in accuracy of image classification is also achieved.