Conclusion
In this study, we propose an end-to-end deep learning method to accomplish image co-segmentation pair-wise. The Siamese encoder network is used to extract the high-level features. The core cross-correlation module is based on depth-wise convolution, which models the common semantic information between images from the perspective of feature similarity matching on each channel. And this module can highlight the center position of the high-level features of common objects. A multi-scale feature pyramid is constructed to improve the model’s adaptability for objects of different sizes. We conducted the experiments on several public datasets. The experimental results show that our approach achieves state-of-the-art performance and can well accomplish the image co-segmentation task. Additionally, several groups of ablation experiments are designed to show the segmentation effect under different hyperparameters. The results show a good effect based on the cross-correlation operation of the pyramid features. Please see Appendixes A–C for details.
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References
Vicente S, Rother C, Kolmogorov V. Object cosegmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. 2217–2224
Xu H, Lin G, Wang M. A review of recent advances in image co-segmentation techniques. IEEE Access, 2019, 7: 182089–182112
Chen J, Chen Y, Li W, et al. Channel and spatial attention based deep object co-segmentation. Knowledge-Based Syst, 2021, 211: 106550
Chen H, Huang Y, Nakayama H. Semantic aware attention based deep object co-segmentation. In: Proceedings of Asian Conference on Computer Vision, 2018. 435–450
Li B, Wu W, Wang Q, et al. SiamRPN++: evolution of Siamese visual tracking with very deep networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019. 4282–4291
Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant Nos. 61605054, 62077017), Hubei Provincial Natural Science Foundation (Grant No. 2021CFB659), Fundamental Research Funds for the Central Universities (Grant Nos. CCNU22QN011, CCNU20TS032), and Science and Technology Innovation 2030 “New Generation Artificial Intelligence” Major Program (Grant No. 2020AAA0108804).
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Appendixes A–C. The supporting information is available online at info.scichina.com and link.springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.
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Chen, J., Chen, Y., Li, W. et al. Image co-segmentation based on pyramid features cross-correlation network. Sci. China Inf. Sci. 66, 119101 (2023). https://doi.org/10.1007/s11432-021-3515-6
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DOI: https://doi.org/10.1007/s11432-021-3515-6