Integrating Multiple Context and Hybrid Interaction for Salient Object Detection

XIA Chenxing; CHEN Xinyu; SUN Yanguang; GE Bin; FANG Xianjin; GAO Xiuju; ZHANG Yan

doi:10.11999/JEIT230719

Volume 46 Issue 7

Jul. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(7): 2918-2931

XIA Chenxing, CHEN Xinyu, SUN Yanguang, GE Bin, FANG Xianjin, GAO Xiuju, ZHANG Yan. Integrating Multiple Context and Hybrid Interaction for Salient Object Detection[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2918-2931. doi: 10.11999/JEIT230719

Citation:

XIA Chenxing, CHEN Xinyu, SUN Yanguang, GE Bin, FANG Xianjin, GAO Xiuju, ZHANG Yan. Integrating Multiple Context and Hybrid Interaction for Salient Object Detection[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2918-2931. doi: 10.11999/JEIT230719

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Integrating Multiple Context and Hybrid Interaction for Salient Object Detection

doi: 10.11999/JEIT230719

XIA Chenxing^{1, 2, 3},
CHEN Xinyu^{1
,
,},
SUN Yanguang⁴,
GE Bin¹,
FANG Xianjin^{1, 5},
GAO Xiuju⁶,
ZHANG Yan⁷

1.
College of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China
2.
Anhui Puhua Big Data Technology Co., Ltd., Huainan 230031, China
3.
Institute of Energy, Hefei Comprehensive National Science Center, Hefei 230601, China
4.
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
5.
Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230601, China
6.
School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
7.
School of Electronics and Information Engineering, Anhui University, Hefei 230601, China

Funds: The National Natural Science Foundation of China (62102003), The Natural Science Foundation of Anhui Province (2108085QF258), Anhui Postdoctoral Science Foundation (2022B623), Huainan City Science and Technology Plan Project (2023A316), The University Synergy Innovation Program of Anhui Province (GXXT-2021-006, GXXT-2022-038), The University-level General Projects of Anhui University of Science and Technology (xjyb2020-04), The Central Guiding Local Technology Development Special Funds (202107d06020001)

Received Date: 2023-07-18
Rev Recd Date: 2024-01-06

Available Online: 2024-01-28

Publish Date: 2024-07-29

Abstract

Abstract

Salient Object Detection (SOD) aims to recognize and segment visual salient objects in images, which is one of the important research contents in computer vision tasks and related fields. Existing Fully Convolutional Networks (FCNs)-based SOD methods have achieved good performance. However, the types and sizes of salient objects are variable and unfixed in real-world scenes, which makes it still a huge challenge to detect and segment salient objects accurately and completely. For that, in this paper, a novel integrating multiple context and hybrid interaction for SOD task is proposed to efficiently predict salient objects by collaborating Dense Context Information Exploration (DCIE) module and Multi-source Feature Hybrid Interaction (MFHI) module. The DCIE module uses dilated convolution, asymmetric convolution and dense guided connection to progressively capture the strongly correlated multi-scale and multi-receptive field context information, and enhances the expression ability of each initial input feature by aggregating context information. The MFHI module contains diverse feature aggregation operations, which can adaptively interact with complementary information from multi-level features to generate high-quality feature representations for accurately predicting saliency maps. The performance of the proposed method is tested on five public datasets. The performance of the proposed method is tested on five public datasets. Experimental results demonstrate that our method achieves superior prediction performance compared with 19 state-of-the-art SOD methods under different evaluation metrics.
- Computer vision,
- Salient Object Detection (SOD),
- Fully Convolutional Networks (FCNs),
- Context information

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References(49)

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