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A Study on Product Search Service using Feature Point Information based on Image

이미지 기반의 특징점 정보를 이용한 제품 검색 서비스에 관한 연구

  • 김석수 (한남대학교 멀티미디어학부)
  • Received : 2019.07.12
  • Accepted : 2019.09.20
  • Published : 2019.09.27

Abstract

With the development of ICT technology and the promotion of smartphone penetration, purchasing services that purchase various products through online market are being activated. In particular, due to advances in storage and delivery technology, sales of short food materials can be purchased online. Therefore, in this paper, we propose an integrated solution that enables advertisement effect, ordering and delivery through a purchase service even if there is no sales knowledge and sales network in a small shopping mall where only offline sales can be performed. The proposed system is able to efficiently view the product information by category through image search for the product that the user desires, so that the seller of the registered product can efficiently sell without any additional advertisement.

ICT 기술의 발전과 스마트폰 보급의 활성화로 온라인 마켓을 통해 다양한 제품을 구매하는 구매 서비스가 활성화되고 있다. 특히 보관 및 배송 기술의 발전으로 인하여 보관기관이 짧은 식자재의 경우도 온라인을 통해 구매가 가능함으로써 오프라인 판매만을 수행하는 상가의 경우 매출이 감소되고 있는 추세이다. 따라서 본 논문에서는 기존 오프라인 판매만 수행이 가능한 소규모 상가에서 전문적인 판매 지식 및 판매망이 없어도 구매 서비스를 통해 광고 효과 및 주문, 배달이 가능한 통합 솔루션을 제안한다. 제안하는 시스템은 사용자가 원하는 제품에 대한 이미지 검색을 통해 효율적으로 제품에 대한 정보를 카테고리별로 볼 수 있으며, 이로 인해 등록된 제품 판매처가 추가적인 광고가 없어도 효율적으로 판매가 가능하다는 장점이 있다.

Keywords

References

  1. S. Y. Jeong, W. Jin Lee & S. Joo Koh. (2017). A Study of Product Recommendation System Using Curation Service Based on Users Ordering Pattern in Business to Business Electronic Commerce. Proceedings of Symposium of the Korean Institute of communications and Information Sciences, 1503-1504.
  2. T. H. Kim, C. B. Moon, B. M. Kim, H. A. Lee & H. S. Kim. (2012). Construction of information management system for user customized manufacturing process. Journal of the Korea Industrial Information Systems Research, 17(2), 45-55. DOI : 10.9723/jksiis.2012.17.2.045
  3. G. S. Lee, Y. H. You & K. S. Jo. (2004). Comparison Shopping System using Image Retrieval on the Semantic Web. Spring Conference of Korean Institute of Information Scientists and Engineers, 31(1-B), 556-558.
  4. C. Liu, L. C. Chen, F. Schroff, H. Adam, W. Hua, A. L. Yuille & L. Fei-Fei. (2019). Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 82-92.
  5. NAVER Search & Tech. (2019). Greedot. https://blog.naver.com/naver_search/221475296426
  6. M. Teichmann, A. Araujo, M. Zhu & J. Sim. (2019). Detect-to-retrieve: Efficient regional aggregation for image search. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5109-5118.
  7. M. Niemeyer & O. Arandjelovic. (2018). Automatic Semantic Labelling of Images by Their Content Using Non-Parametric Bayesian Machine Learning and Image Search Using Synthetically Generated Image Collages. In 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), 160-168.
  8. S. Sheik, S. K. Aggarwal, A. Poddar, N. Balakrishnan & K. Sekar. (2004). A fast pattern matching algorithm. Journal of chemical information and computer sciences, 44(4), 1251-1256. https://doi.org/10.1021/ci030463z
  9. D. S. Dev & D. R. Kisku. (2017). Improved Pattern Matching Algorithm. Applied Mathematics & Information Sciences An International Journal, 11(4), 1163-1184. https://doi.org/10.18576/amis/110424
  10. J. Matas, C. Galambos & J. Kittler. (2000). Robust detection of lines using the progressive probabilistic hough transform. Computer Vision and Image Understanding, 78(1), 119-137. DOI : 10.1006/cviu.1999.0831
  11. J. Wang, P. Fu & R. X. Gao. (2019). Machine vision intelligence for product defect inspection based on deep learning and Hough transform. Journal of Manufacturing Systems, 51, 52-60. DOI : 10.1016/j.jmsy.2019.03.002
  12. R. Sundaram, P. Jayaraman, R. Rangarajan, R. Rengasri, C. Rajeshwari & K. S. Ravichandran. (2019). Automated Optic Papilla Segmentation Approach Using Normalized Otsu Thresholding. Journal of Medical Imaging and Health Informatics, 9(7), 1346-1353. DOI : 10.1166/jmihi.2019.2783
  13. C. Long, R. Collins, E. Swears & A. Hoogs. (2019). Deep Neural Networks In Fully Connected CRF For Image Labeling With Social Network Metadata. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 1607-1615.
  14. J. S. Kang, J. W. Baek & K. Y. Jung. (2019). Multimodal Media Content Classification using Keyword Weighting for Recommendation. Journal of Convergence for Information Technology, 9(5), 1-6. DOI : 10.22156/CS4SMB.2019.9.5.001
  15. D. H. Kim, S. S. Kim & E. J. Choi. (2019). Design of Home Furnishing Simulation System using Real Space Information. Journal of Convergence for Information Technology, 9(1), 151-157. DOI : 10.22156/CS4SMB.2019.9.1.151
  16. B. T. Ahn. (2019). A Study on Unmanned Image Tracking System based on Smart Phone. Journal of Convergence for Information Technology, 9(3), 30-35. DOI : 10.22156/CS4SMB.2019.9.3.030
  17. M. N. Ellingham & J. A. Ellis-Monaghan. (2019). Edge-outer graph embedding and the complexity of the DNA reporter strand problem. Theoretical Computer Science.
  18. U. Bhowan, P. Sacristan, L. O'Malley, A.A. Miranda & M.Corcoran. (2019). U.S. Patent Application No. 15/810,885.
  19. Y. Jin. (2019). Applying Fuzzy Clustering to Examine Marketing Strategy of Tourism Brand in Mobile Internet Era. Journal of Electronic Commerce in Organizations (JECO), 17(2), 29-41. https://doi.org/10.4018/JECO.2019040103
  20. B. W. Min. (2018). Review of Hazard Mapping Information Collected for Local Disaster Prevention by Residents in a Historical Local Town, Journal of Convergence for Information Technology, 8(4), 121-126. https://doi.org/10.22156/cs4smb.2018.8.4.121