Journal
PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA (MM'19)
Volume -, Issue -, Pages 1375-1382Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3343031.3350860
Keywords
Graph convolutional network; image super-pixel; co-saliency detection; graph learning
Funding
- NSFC Key Projects of International (Regional) Cooperation and Exchanges [61860206004]
- National Natural Science Foundation of China [61602001, 61872005, 61671018]
- Natural Science Foundation of Anhui Province [1708085QF139]
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Co-saliency estimation which aims to identify the common salient object regions contained in an image set is an active problem in computer vision. The main challenge for co-saliency estimation problem is how to exploit the salient cues of both intra-image and inter-image simultaneously. In this paper, we first represent intra-image and inter-image as intra-graph and inter-graph respectively and formulate co-saliency estimation as graph nodes labeling. Then, we propose a novel multiple graph learning and convolutional network (M-GLCN) for image co-saliency estimation. M-GLCN conducts graph convolutional learning and labeling on both inter-graph and intra-graph cooperatively and thus can well exploit the salient cues of both intra-image and inter-image simultaneously for co-saliency estimation. Moreover, M-GLCN employs a new graph learning mechanism to learn both inter-graph and intra-graph adaptively. Experimental results on several benchmark datasets demonstrate the effectiveness of M-GLCN on co-saliency estimation task.
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