Abstract
This thesis presents a nonparametric scene parsing system based on superpixel matching and exemplar cut. Foreground classes are often neglected in other algorithms since they occupy only a small portion of the pixels in an image. To solve this problem, we utilize the concept of “exemplar” to improve their recognition rate. Our experimental images are unique as we photograph continuously from a moving vehicle. Thus, the characteristics of progressive images can be utilized to raise labeling accuracy. By adding the previous parsing result into retrieval set, we enhance the resemblance between query image and images in the retrieval set. We also remove the pictures which have large class proportion discrepancy compared with previous frame, which prevents the unlikely classes to appear on the query image. And we add exemplars in the previous image to candidate exemplars of query image. This novel idea can hopefully be applied on autonomous car driving in the near future. Our experimental dataset contains 4 foreground labels and 4 background labels. The system achieves state-of-the-art recognition rate on both per-pixel accuracy and per-class accuracy.