Salient object detection algorithm based on diversity features and global guidance information

Fan Shao; Kai Wang; Yanluo Liu

doi:10.61187/ita.v1i1.14

Authors

Fan Shao Shaanxi University of Science and Technology, School of Electronic Information and Artificial Intelligence, Xi 'an, Shaanxi 710021
Kai Wang Shaanxi University of Science and Technology, School of Electronic Information and Artificial Intelligence, Xi 'an, Shaanxi 710021
Yanluo Liu Shaanxi University of Science and Technology, School of Electronic Information and Artificial Intelligence, Xi 'an, Shaanxi 710021

Keywords:

Salient object detection, Global information guidance, Diversity character, Feature fusion

Abstract

Aiming at the problems of traditional salient object detection methods such as fuzzy boundary and insufficient information integrity, a salient object detection network composed of feature diversity enhancement module, global information guidance module and feature fusion module is proposed. Firstly, asymmetric convolution, cavity convolution and common convolution are spliced to form a feature diversity enhancement module to extract different types of spatial features corresponding to each feature layer. Secondly, the global information guidance module transmits the information captured by the coordinate attention mechanism to each feature layer through the global guidance stream, so as to learn the semantic relationship between different feature layers and alleviate the dilution effect; Finally, the feature fusion module receives the high-level features output from the previous layer, the low-level features of the corresponding layer and the global context information generated by the global information guidance module, and the cascade feature diversity enhancement module gradually integrates the multi-level features to refine the saliency feature map. Comparative experiments on five public data sets show that this method can achieve the highest values of 0.959 and 0.030 in F-measure and MAE. Compared with other seven advanced algorithms, it has better detection performance.

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References

Cheng M M, Zhang F L, Mitra N J, et al. Repfinder: finding approximately repeated scene elements for image editing[J]. ACM transactions on graphics (TOG), 2010, 29(4): 1-8. DOI: https://doi.org/10.1145/1778765.1778820

Wang Q, Tang S, Zhai D, et al. Salience based object tracking in complex scenes[J]. Neurocomputing, 2018, 314: 132-142. DOI: https://doi.org/10.1016/j.neucom.2018.05.102

Cheng M M, Mitra N J, Huang X, et al. Global contrast based salient region detection[J]. IEEE transactions on pattern analysis and machine intelligence, 2014, 37(3): 569-582. DOI: https://doi.org/10.1109/TPAMI.2014.2345401

Liu R, Cao J, Lin Z, et al. Adaptive partial differential equation learning for visual saliency detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2014: 3866-3873. DOI: https://doi.org/10.1109/CVPR.2014.494

Ma M, Xia C, Li J. Pyramidal feature shrinking for salient object detection[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2021, 35(3): 2311-2318. DOI: https://doi.org/10.1609/aaai.v35i3.16331

Qin X, Zhang Z, Huang C, et al. Basnet: Boundary-aware salient object detection[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019: 7479-7489. DOI: https://doi.org/10.1109/CVPR.2019.00766

Wu Z, Su L, Huang Q. Cascaded partial decoder for fast and accurate salient object detection[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019: 3907-3916. DOI: https://doi.org/10.1109/CVPR.2019.00403

Wang Zhengwen, Song Huihui, Fan Jiaqing, et al.Salient Target Detection Network Based on Semantic Guided Feature Aggregation[J]. Acta automatica sinica,2021,48:1001-1010.

Chen Z, Xu Q, Cong R, et al. Global context-aware progressive aggregation network for salient object detection[C]//Proceedings of the AAAI conference on artificial intelligence. 2020, 34(07): 10599-10606. DOI: https://doi.org/10.1609/aaai.v34i07.6633

Zhou H, Xie X, Lai J H, et al. Interactive two-stream decoder for accurate and fast saliency detection[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 9141-9150. DOI: https://doi.org/10.1109/CVPR42600.2020.00916

Darren Liu, Guo Jichang, Wang Yudong, et al.Multi-scale saliency target detection network based on attention mechanism[J]. Journal of xidian university (Natural Science Edition) ,2022,49(4):118-126.

Liu J J, Hou Q, Cheng M M, et al. A simple pooling-based design for real-time salient object detection[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019: 3917-3926. DOI: https://doi.org/10.1109/CVPR.2019.00404

Qin X, Zhang Z, Huang C, et al. U2-Net: Going deeper with nested U-structure for salient object detection[J]. Pattern recognition, 2020, 106: 107404. DOI: https://doi.org/10.1016/j.patcog.2020.107404

Zhuge M, Fan D P, Liu N, et al. Salient object detection via integrity learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. DOI: https://doi.org/10.1109/TPAMI.2022.3179526

He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778. DOI: https://doi.org/10.1109/CVPR.2016.90

X. Ding, Y. Guo, G. Ding, and J. Han, “ACNet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks,” in IEEE Int. Conf. Comput. Vis., 2019, pp. 1911–1920. DOI: https://doi.org/10.1109/ICCV.2019.00200

Wang P, Chen P, Yuan Y, et al. Understanding convolution for semantic segmentation[C]//2018 IEEE winter conference on applications of computer vision (WACV). Ieee, 2018: 1451-1460. DOI: https://doi.org/10.1109/WACV.2018.00163

Hou Q, Zhou D, Feng J. Coordinate attention for efficient mobile network design. arXiv 2021[J]. arXiv preprint arXiv:2103.02907, 2021. DOI: https://doi.org/10.1109/CVPR46437.2021.01350

De Boer P T, Kroese D P, Mannor S, et al. A tutorial on the cross-entropy method[J]. Annals of operations research, 2005, 134(1): 19-67. DOI: https://doi.org/10.1007/s10479-005-5724-z

Wang Z, Simoncelli E P, Bovik A C. Multiscale structural similarity for image quality assessment[C]//The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003. Ieee, 2003, 2: 1398-1402.

Rahman M A, Wang Y.Optimizing intersection overunion in deep neural networks for image segmentation[C]//International symposium on visual computing. Springer, Cham, 2016: 234-244. DOI: https://doi.org/10.1007/978-3-319-50835-1_22

Kanopoulos N, Vasanthavada N, Baker R L. Design of an image edge detection filter using the Sobel operator[J]. IEEE Journal of solid-state circuits, 1988, 23(2): 358-367. DOI: https://doi.org/10.1109/4.996

Li Y, Hou X, Koch C, et al. The secrets of salient object segmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2014: 280-287. DOI: https://doi.org/10.1109/CVPR.2014.43

Shi J, Yan Q, Xu L, et al. Hierarchical image saliency detection on extended CSSD[J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 38(4): 717-729. DOI: https://doi.org/10.1109/TPAMI.2015.2465960

Movahedi V, Elder J H. Design and perceptual validation of performance measures for salient object segmentation[C]//2010 IEEE computer society conference on computer vision and pattern recognition-workshops. IEEE, 2010: 49-56. DOI: https://doi.org/10.1109/CVPRW.2010.5543739

Yang C, Zhang L, Lu H, et al. Saliency detection via graph-based manifold ranking[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2013: 3166-3173. DOI: https://doi.org/10.1109/CVPR.2013.407

Ronneberger O, Fischer P, Brox T, et al. U-net: Convolutional networks for biomedical image segmentation[C]//International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015: 234-241. DOI: https://doi.org/10.1007/978-3-319-24574-4_28