A novel multi-graph framework for salient object detection

Graph-based methods have been widely adopted for predicting the most attractive region in an image. Most of the existing graph-based methods only utilize single graph to describe the image information, and thus cannot adapt for complex scenes. In this paper, a novel multi-graph framework for salient...

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Bibliographic Details
Published in:The Visual computer 2019-11, Vol.35 (11), p.1683-1699
Main Authors: Lu, Ye, Zhou, Kedong, Wu, Xiyin, Gong, Penghan
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Graphics
Computer Science
Deep learning
Graphical representations
Image Processing and Computer Vision
Image retrieval
Methods
Object recognition
Original Article
Propagation
Salience
Seeds
Semantics
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Summary:Graph-based methods have been widely adopted for predicting the most attractive region in an image. Most of the existing graph-based methods only utilize single graph to describe the image information, and thus cannot adapt for complex scenes. In this paper, a novel multi-graph framework for salient object detection is proposed. The proposed method is divided into three steps. Firstly, an image is divided into superpixels and described as a multi-graph, where superpixels are represented as nodes and their information is computed by color space and location space. Secondly, the multiple graphs are combined into a novel multi-graph-based manifold ranking propagation framework to obtain a coarse map. Finally, a map refinement model is developed to improve the quality of the coarse map. Experimental results on four challenging datasets show that the proposed method performs favorably against the state-of-the-art salient object detection methods.
ISSN:0178-2789
1432-2315
DOI:10.1007/s00371-019-01637-2