Scene-Aware Deep Networks for Semantic Segmentation of Images - Zhike Yi; Tao Chang; Shuai Li; Ruijun Liu; Jing Zhang; Aimin Hao (IEEE Access)
Scene classification and semantic segmentation are two important research directions in computer vision. They are widely used in the research of automatic driving and human–computer interaction. The purpose of the scene classification is to use the image classification to determine the category of the scene in an image by analyzing the background and the target object, while semantic segmentation aims to classify the image at the pixel level and mark the position and semantic information of the scene unit. In this paper, we aimed to train the semantic segmentation neural network in different scenarios to obtain the models with the same number of scene categories, which they are used to process the images. During the process of the actual test, the semantic segmentation dataset was firstly divided into three categories based on the scene classification algorithm. Then the semantic segmentation neural network is trained under three scenarios, and three semantic segmentation network models are obtained accordingly. To test the property of our methods, the semantic segmentation models we got were selected to treat other pictures, and the results obtained from the performance of scene-aware semantic segmentation were much better than semantic segmentation without considering categories. Our study provided an essential improvement of semantic segmentation by adding category information into consideration, which will be helpful to obtain more precise models for further picture analysis.