Abstract
In order to enhance the image understanding of different regions for national costume grayscale image automatic colorization, let coloring tasks take advantage of semantic conditions, also let it can apply the human parsing semantic segmentation method to the national costume grayscale image for semantic segmentation task. This paper proposes a semantic segmentation model for context embedding based on edge perceiving. Aiming at the features of national costume grayscale image, more optimizing the model and loss function. The national costume grayscale image semantic segmentation is different from semantic segmentation of the color image, this task is more difficult for the grayscale image has no color feature. In this paper, edge information and edge consistency constraints are used to improve the national costume grayscale image coloring effect. The experimental results show that the model designed in this paper can obtain more accurate fine-grained semantic segmentation results for the national costume grayscale image.
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Acknowledgment
This work is supported by National Natural Science Foundation of China (No. 61862068), Yunnan Expert Workstation of Xiaochun Cao, and Scientific Technology Innovation Team of Educational Big Data Application Technology in University of Yunnan Province.
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Wu, D., Gan, J., Zou, W. (2021). Fine-Grained Semantic Segmentation of National Costume Grayscale Image Based on Human Parsing. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds) Data Mining and Big Data. DMBD 2021. Communications in Computer and Information Science, vol 1453. Springer, Singapore. https://doi.org/10.1007/978-981-16-7476-1_26
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DOI: https://doi.org/10.1007/978-981-16-7476-1_26
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