Image Saliency Detection Based on Object Compactness and Regional Homogeneity Strategy

Hongmei TANG; Biying WANG; Liying HAN; Yatong ZHOU

doi:10.11999/JEIT190101

Volume 41 Issue 10

Oct. 2019

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Hongmei TANG, Biying WANG, Liying HAN, Yatong ZHOU. Image Saliency Detection Based on Object Compactness and Regional Homogeneity Strategy[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2532-2540. doi: 10.11999/JEIT190101

Citation:

Hongmei TANG, Biying WANG, Liying HAN, Yatong ZHOU. Image Saliency Detection Based on Object Compactness and Regional Homogeneity Strategy[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2532-2540. doi: 10.11999/JEIT190101

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Image Saliency Detection Based on Object Compactness and Regional Homogeneity Strategy

doi: 10.11999/JEIT190101

School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China

Funds: Chunhui project of the Ministry of Education (Z2017015)

Received Date: 2019-02-21
Rev Recd Date: 2019-05-28

Available Online: 2019-06-04

Publish Date: 2019-10-01

Abstract

Abstract

Considering the inaccurate description of feature differences between nodes in the graph-based saliency detection algorithm, an image saliency detection algorithm combining object compactness and regional homogeneity strategy is proposed. Different from the commonly used graph-based model, a sparse graph-based structure closer to the human visual system and a novel regional homogeneity graph-based structure are established. They are used to describe the correlation within the foreground and the difference between foreground and background. Therefore, many redundant connections of nodes are eliminated and the local spatial relationship of nodes is strengthened. Then the clusters are combined to form a saliency map by means of manifold ranking. Finally, the background confidence is introduced for saliency optimization by the similarity of the background region clusters and the final detection result is obtained. Compared with 4 popular graph-based algorithms on the four benchmark datasets, the proposed algorithm can highlight the salient regions clearly and has better performance in the evaluation of multiple comprehensive indicators.
- Graph-based model,
- Object compactness,
- Regional homogeneity,
- Manifold ranking,
- Saliency detection

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References(15)

References

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