Semantic segmentation of high-resolution images

作     者：Juhong WANG Bin LIU Kun XU 

作者机构：Department of Computer Science and Technology Tsinghua University 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2017年第60卷第12期

页      面：256-261页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：supported by National Natural Science Foundation of China(Grant No.61521002) a research grant from the Beijing Higher Institution Engineering Research Center the TsinghuaTencent Joint Laboratory for Internet Innovation Technology 

主　　题：image semantic segmentation high-resolution images joint bilateral upsampling 

摘      要：Image semantic segmentation is a research topic that has emerged recently. Although existing approaches have achieved satisfactory accuracy, they are limited to handling low-resolution images owing to their large memory consumption. In this paper, we present a semantic segmentation method for high-resolution images. First, we downsample the input image to a lower resolution and then obtain a low-resolution semantic segmentation image using state-of-the-art methods. Next, we use joint bilateral upsampling to upsample the low-resolution solution and obtain a high-resolution semantic segmentation image. To modify joint bilateral upsampling to handle discrete semantic segmentation data, we propose using voting instead of interpolation in filtering computation. Compared to state-of-the-art methods, our method significantly reduces memory cost without reducing result quality.