A novel multi-graph framework for salient object detection

Ye Lu

doi:10.1007/s00371-019-01637-2

A novel multi-graph framework for salient object detection

2019-02-13

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AI

DOI：10.1007/s00371-019-01637-2

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Abstract

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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.

Keywords:

Salient object detection

Multi-graph framework

Map refinement

Manifold ranking

A novel multi-graph framework for salient object detection

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