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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.3
- 발행연도
- 2023.6
- 수록면
- 206 - 214 (9page)
- DOI
- 10.5573/IEIESPC.2023.12.3.206
이용수
초록· 키워드
오류제보하기
Automatic labeling of incomplete images can reduce the impact of image defects on understanding image content, which has great research value. The traditional image annotation method is manual annotation, which is inefficient, heavy, and subjective work. Therefore, an automatic image labeling algorithm based on mobile computing environment is proposed. The algorithm involves image preprocessing, feature extraction, similarity measurement of image features, and model training. In the preprocessing, image fragments are removed to reduce the complexity of the algorithm and increase the accuracy of automatic annotation. The image feature extraction was implemented by the scaling invariant feature transformation (SIFT) algorithm, and the image annotation algorithm was constructed based on the similarity between tag words and an image. Experimental results show that the recall rate of the algorithm reaches 97%, and the standard deviation of the normal distribution of automatic labeling after 300 iterations was 11. The results show that compared with the existing image automatic annotation methods, the proposed image automatic annotation algorithm has higher accuracy and better performance.
목차
Abstract
1. Introduction
2. Overall Design of Automatic Marking of Incomplete Images in Mobile Computing Environment
3. Design of Automatic Labeling Algorithm for Incomplete Image
4. Experimental Verification
5. Conclusion
References
참고문헌 (18)
참고문헌 신청
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W. Z. Liang, I. Possignolo, X. Qiao, et al. “Utilizing digital image processing and two-source energy balance model for the estimation of evapotranspiration of dry edible beans in western Nebraska,” Irrigation Science, 2021:1-15. Article (CrossRef Link)
C. D. Tormena, R. C. S. Campos, G. G. “Marcheafave, et al. Authentication of carioca common bean cultivars (Phaseolus vulgaris L.) using digital image processing and chemometric tools,” Food Chemistry, 2021, 364(1):130349. Article (CrossRef Link)
T. M. Svahn, R. Gordon, J. C. Ast, et al. “Comparison of Photon-Counting and Flat-Panel Digital Mammmography for The Purpose of 3d Imaging Using a Novel Image Processing Method,” Radiation Protection Dosimetry, 2021(3-4):3-4. Article (CrossRef Link)
M. Talaat, M. Tayseer, A. Elzein. “Digital image processing for physical basis analysis of electrical failure forecasting in XLPE power cables based on field simulation using finite-element method,” IET Generation Transmission & Distribution, 2020, 14(26)
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