Scene parsing is an important and challenging problem in computer vision. It requires labeling each pixel in an image with the category it belongs to. Traditionally, it has been approached with hand-engineered features from color information in images. Recently convolutional neural networks (CNNs), which automatically learn hierarchies of features, have achieved record performance on the task. These approaches typically include a post-processing technique, such as superpixels, to produce the final labeling. In this paper, we propose a novel network architecture that combines deep deconvolutional neural networks with CNNs. Our experiments show that deconvolutional neural networks are capable of learning higher order image structure beyond edge primitives in comparison to CNNs. The new network architecture is employed for multi-patch training, introduced as part of this work. Multi-patch training makes it possible to effectively learn spatial priors from scenes. The proposed approach yields state-of-the-art performance on four scene parsing datasets, namely Stanford Background, SIFT Flow, CamVid, and KITTI. In addition, our system has the added advantage of having a training system that can be completely automated end-to-end without requiring any post-processing.
In REF , Mohan proposed combining deep deconvolutional neural networks with CNNs for road scene parsing, and good performance was achieved.
Deep Deconvolutional Networks for Scene Parsing
ArXiv
ArXiv
Computer Science Mathematics