期刊
2019 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME)
卷 -, 期 -, 页码 332-337出版社
IEEE
DOI: 10.1109/ICME.2019.00065
关键词
Graph convolutional network; Semi-supervised learning; Co-saliency detection; Superpixel
类别
资金
- National Natural Science Foundation of China [61602001]
- Shenzhen Science AMP
- Research Project [JCYJ20170817160058246]
Recently, Graph Convolutional Networks (GCNs) have been usually utilized for graph data representation in computer vision area. However, existing graph GCNs generally use a single graph which can be not adapted for the data with multiple graphs. In this paper, we first propose a novel multiple graph convolutional network (MGCN) for multiple graph data representation and learning. MGCN propagates information/knowledge across multiple graphs and obtains a consistent representation and learning by integrating the information of multiple graphs simultaneously. Based on the proposed MGCN, we then propose a new global-local unified graph convolutional learning architecture for image co-saliency detection problem. The main benefits of the proposed co-saliency model are twofold. First, it learns an optimal superpixel feature representation for co-saliency detection problem. Second, it can well exploit both intra-image and inter-image cues for co-saliency detection via a unified network. Promising experiments demonstrate the effectiveness of the proposed MGCN based co-saliency detection approach.
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