文献类型：期刊文献

英文题名：Hierarchical semantic segmentation of image scene with object labeling

作者：Li, Qing[1];Liang, Aihua[1];Liu, Hongzhe[1]

通讯作者：Li, Q[1]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]corresponding author), Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China.|[11417103]北京联合大学北京市信息服务工程重点实验室;[11417]北京联合大学;

年份：2018

卷号：2018

期号：1

外文期刊名：EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING

收录：;EI(收录号：20181004879077);Scopus(收录号：2-s2.0-85042851406);WOS:【SCI-EXPANDED(收录号:WOS:000426821100001)】；

基金：This work is supported by National Natural Science Foundation of China (61502036), the General Project of Scientific Research Project of the Beijing Education Committee(KM201611417015), and Open Funding of Beijing Key Laboratory of Information Service Engineering (Zk20201502).

语种：英文

外文关键词：Semantic labeling; Object labeling; Semantic segmentation

摘要：Semantic segmentation of an image scene provides semantic information of image regions while less information of objects. In this paper, we propose a method of hierarchical semantic segmentation, including scene level and object level, which aims at labeling both scene regions and objects in an image. In the scene level, we use a feature-based MRF model to recognize the scene categories. The raw probability for each category is predicted via a one-vs-all classification mode. The features and raw probability of superpixels are embedded into the MRF model. With the graph-cut inference, we get the raw scene-level labeling result. In the object level, we use a constraint-based geodesic propagation to get object segmentation. The category and appearance features are utilized as the prior constraints to guide the direction of object label propagation. In this hierarchical model, the scene-level labeling and the object-level labeling have a mutual relationship, which regions and objects are optimized interactively. The experimental results on two datasets show the well performance of our method.