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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.12 No.2
- 발행연도
- 2023.4
- 수록면
- 87 - 96 (10page)
- DOI
- 10.5573/IEIESPC.2023.12.2.87
이용수
초록· 키워드
오류제보하기
There are some problems in oracle image classification, such as high image-feature extraction error and low classification accuracy, which leads to low efficiency. Therefore, we designed an automatic classification method for oracle images based on deep learning. The clustering center of an oracle image is determined using k-means algorithm. The obtained oracle image block is used as parallel input to extract a dictionary. The oracle image features are output through a feature mapping function for feature extraction. The CS algorithm is used to reduce the dimension of the oracle image, and the oracle feature compressed sensing signal is sparse under a certain basis for preprocessing of image features. The center of six feature points in the features is used to describe the center of the feature points of the whole oracle image, to determine the position change state of the feature points, and to characterize the key features of the image to achieve the target alignment of oracle image feature points. Based on this, the image feature data are learned using a transfer learning model. The automatic classifier was constructed through a convolution neural network. Experimental results show that the proposed method can effectively reduce the error of image feature extraction and improve the classification accuracy. At the application level, the research results of this paper have brought great improvement to the performance of oracle image classification technology. At the theoretical level, this research introduces k-means and CS algorithms to oracle image classification technology based on deep learning and makes an effective exploration in the direction of this technological progress.
목차
Abstract
1. Introduction
2. Feature Extraction and Preprocessing of Oracle Bone Inscriptions
3. Research on Automatic Classification of Oracle Images based on Deep Learning
4. Experimental Analyses
5. Conclusions
References
참고문헌 (14)
참고문헌 신청
Xiong. J, Liu. G, Liu. Y, Liu. M. “Oracle Bone Inscriptions information processing based on multimodal knowledge graph,” Computers & Electrical Engineering, vol. 92, no. 6, pp. 107173, 2021.Article (CrossRef Link)
Wang. J.X. “The Pattern of Religious Culture: Toward a New Theory for China's Ethnological and Anthropological Studies,” Nationalities Research in Qinghai, vol. 4, pp. 1-14, 2007. Article (CrossRef Link)
Procopio. N, Hopkins. R, Harvey. VL, Buckley, M. “Proteome variation with Collagen yield in ancient bone,” Journal of Proteome Research, vol. 20 no.3, pp. 1754-1759, 2021. Article (CrossRef Link)
Bejiga. MB, Melgani. F, Vascotto. A. “Retro-remote sensing: Generating images from ancient texts,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 3, pp. 950-960, 2019. Article (CrossRef Link)
Wang. K, Yi. Y, Tang. Z, Peng. J. “Multi-scene ancient Chinese text recognition with deep coupled alignments,” Applied Soft Computing, vol. 108, no. 4, pp. 107475, 2021. Article (CrossRef Link)
Wang. J.X. “The Pattern of Religious Culture: Toward a New Theory for China's Ethnological and Anthropological Studies,” Nationalities Research in Qinghai, vol. 4, pp. 1-14, 2007. Article (CrossRef Link)
Procopio. N, Hopkins. R, Harvey. VL, Buckley, M. “Proteome variation with Collagen yield in ancient bone,” Journal of Proteome Research, vol. 20 no.3, pp. 1754-1759, 2021. Article (CrossRef Link)
Bejiga. MB, Melgani. F, Vascotto. A. “Retro-remote sensing: Generating images from ancient texts,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 3, pp. 950-960, 2019. Article (CrossRef Link)
Wang. K, Yi. Y, Tang. Z, Peng. J. “Multi-scene ancient Chinese text recognition with deep coupled alignments,” Applied Soft Computing, vol. 108, no. 4, pp. 107475, 2021. Article (CrossRef Link)
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