Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
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Study of the New Distance for Image Retrieval

새로운 이미지 거리를 통한 이미지 검색 방안 연구

  • Received : 2013.12.04
  • Accepted : 2014.05.07
  • Published : 2014.08.15
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Abstract

Image retrieval is a procedure to find images based on the resemblance between query image and all images. In retrieving images, the crucial step that arises is how to define the similarity between images. In this paper, we propose a new similarity measure which is based on distribution of color. We apply the new measure to retrieving two different types of images, wallpaper images and the logo of automobiles, and compare its performance to other existing similarity measures.

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References

  1. Antani, S., Kasturi, R., and Jain, R. (1998), Pattern Recognition Methods in Image and Video Databases : Past, Present and Future, Proceedings of SPR '98 and SSPR '98, 1451, 31-53.
  2. Ban, J.-O., Kang, M.-J., and Choi, H.-J. (2002), Design and Implementation of Content-based Image Retrieval System using Color Spatial and Shape Information, Proceedings of KIISE, 29(1), 613-615.
  3. Enser, P. G. B. (1995), Pictorial information retrieval, Journal of Documentation, 51(2), 126-170. https://doi.org/10.1108/eb026946
  4. Song, M.-S. and Park, C.-S. (1998), Nonparametric Statistical Methods, Jayou Academy, Seoul, Korea.
  5. Goshtasby, A. A. (2012), Image Registration : Principles, Tools and Methods, Springer , Newyork , USA.
  6. Gonzalez, R. C., Woods, R. E., and Eddins, S. L. (2012), Digital Image Processing using MATLAB, McGraw-Hill, Columbus, USA.
  7. Law, A. M. (2007), Simulation Modeling and Analysis, McGraw-Hill, New York, USA.
  8. Mo, Y.-I. and Lee, C.-G. (2009), Increasing the Efficiency of Image Search Using Image Attribute in the area of content-Based Image Retrieval, Journal of the Korea Society for Simulation, 18(2), 39-48.
  9. Nah, Y.-M. (2003), Image Content Modeling for Meaning-based Retrieval, Journal of KISS : Databases, 30(2), 145-156.
  10. Rajshree, S. D., Niket, B., and Rajnish, C. (2010), Image Mining using Content Based Image Retrieval System, International Journal on Computer Science and Engineering, 2(7), 2353-2356.
  11. Song, J.-Y., Kim, W.-C., Kim, S.-W., and Park, S.-H. (2007), A Image Retrieval Model Based on Weighted Visual Features Determined by Relevance Feedback, Journal of KISS : Databases, 34(3), 193- 205.
  12. Swain, M. J. and Ballard, D. H. (1991), Color indexing, International Journal of Computer Vision, 7(1), 11-32. https://doi.org/10.1007/BF00130487
  13. Rand, W. M. (1971). Objective criteria for the evaluation of clustering methods, Journal of the American Statistical Association, 66, 846-850. https://doi.org/10.1080/01621459.1971.10482356
  14. Rhee, D.-H., Ryoo, K.-S., and Kim, W.-Y. (2000), Character Image Retrieval using Color and Shape Information, Journal of Broadcast Engineering, 5(1), 50-60.
  15. Yi, S.-K., Hong, S.-E., and Park, S.-H. (2006), A Similar Price Zone Determination of Public Land Price Using a Hybrid Clustering Technique, Journal of Korean Geographical Society, 41(1), 121-135.
  16. Zachar, J., Iyengar, S. S., and Barhen, J. (2001), Content Based Image Retrieval and Information Theory : A General Approach, Journal of the American Society for Information Science and Technology, 52(10), 840-852. https://doi.org/10.1002/asi.1138