디지털융복합연구 (Journal of Digital Convergence)

  • 제15권4호
  • /
  • Pages.267-273
  • /
  • 2017
  • /
  • 2713-6434(pISSN)
  • /
  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
권호 표지
DOI QR코드

DOI QR Code

내용 기반 이미지 검색에서 효율적인 색상-모양 표현을 위한 복소 색상 모델

Complex Color Model for Efficient Representation of Color-Shape in Content-based Image Retrieval

  • 최민석 (삼육대학교 경영정보학과)
  • Choi, Min-Seok (Dept. of Management Information Systems, Sahmyook University)
  • 투고 : 2017.02.14
  • 심사 : 2017.04.20
  • 발행 : 2017.04.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

각종 디지털 기기와 통신 기술의 발전으로 다양한 멀티미디어 콘텐츠의 생산과 유통이 폭발적으로 증가하고 있다. 이미지와 동영상 등의 멀티미디어 데이터의 검색을 위해서는 기존의 문자 위주의 검색과는 다른 접근 방식이 필요하다. 이미지의 여러 가지 물리적인 특징들을 정량화 하여 분석하고 이를 비교하여 유사한 이미지를 검색하는 내용기반 이미지 검색에서 색상과 모양은 주요 물리적 특징들이다. 지금까지는 색상과 모양을 서로 독립적인 특징으로 분리하여 이용하였지만, 인지적 관점에서 두 특징은 밀접한 관련이 있다. 본 논문에서는 색상과 모양 특징을 동시에 표현하기 위하여 3차원 색상 정보를 2차원 복소수 형식으로 표현하는 복소 색상 모델을 이용하여 색상의 공간적 분포 모양을 기술하는 방법을 제안한다. 복소 이미지를 주파수 변환한 후 저주파 영역의 소수의 계수만으로 복원하는 실험을 통하여 제안된 방법이 색상의 공간적 분포 모양을 효율적으로 표현할 수 있음을 보였다.

With the development of various devices and communication technologies, the production and distribution of various multimedia contents are increasing exponentially. In order to retrieve multimedia data such as images and videos, an approach different from conventional text-based retrieval is needed. Color and shape are key features used in content-based image retrieval, which quantifies and analyzes various physical features of images and compares them to search for similar images. Color and shape have been used as independent features, but the two features are closely related in terms of cognition. In this paper, a method of describing the spatial distribution of color using a complex color model that projects three-dimensional color information onto two-dimensional complex form is proposed. Experimental results show that the proposed method can efficiently represent the shape of spatial distribution of colors by frequency transforming the complex image and reconstructing it with only a few coefficients in the low frequency.

키워드

참고문헌

  1. Bo-Seon Kang, Keun-Ho Lee, "Fire Alarm Solutions Through the Convergence of Image Processing Technology and M2M", Journal of the Korea Convergence Society, Vol. 7, No. 1, pp. 37-42, 2016. https://doi.org/10.15207/JKCS.2016.7.1.037
  2. Kang-Hun Lee, Dong-Il Kim, Dae-ho Kim, Myung-Yoon Sung, Young-Kil Lee, Suk-Yong Jung, "Implementation of Real-Time Video Transfer System on Android Environment", Journal of the Korea Convergence Society, Vol. 3, No. 1, pp. 1-5, 2012.
  3. T. Dharani, I. Laurence Aroquiaraj, "A Survey on Content Based Image Retrieval", IEEE, International Conference on Pattern Recognition, Informatics and Mobile Engineering (PRIME), pp. 485-490, February, 2013.
  4. Yoshitaka, A., Ichikawa, T., "A Survey on Content-Based Retrieval for Multimedia Databases", IEEE transactions on knowledge and data engineering, Vol. 11, No. 1, pp. 81-93, 1999. https://doi.org/10.1109/69.755617
  5. MPEG-7 Visual Group, Text of ISO/IEC 15938-3/FDIS Information technology - Multimedia content description interface - Part 3 Visual, ISO/IEC JTC1/ SC29/WG11 N4358, Sydney, July 2001.
  6. Juwan Song, "Content-based Image Retrieval using HSV Color and Uniform Local Binary Patterns", Journal of Korean Institute of Information Technology, Vol. 12, No. 2, pp. 169-174, 2014.
  7. Jeong-Hyun Cho, "Evaluation of Performance for Shape Extraction using Modified Chain Code and Color Information with Spatial Relationship", Journal of KISS : Technology Education, Vol. 1, No. 1. pp. 33-39, 2004.
  8. Seok-Woo Jang, Solima Khanam, Woojin Paik, "Image Retrieval Integrating Interior and Contour Descriptors", Journal of Korean Institute of Information Technology, Vol. 10, No. 1, pp. 209-216, 2012.
  9. Anil K. Jain, Aditya Vailaya, "Image Retrieval using Color and Shape", Pattern Recognition, Vol. 29, No. 8, pp. 1233-1244, 1996. https://doi.org/10.1016/0031-3203(95)00160-3
  10. B. S. Manjunath, Philippe Salembier and Thomas Sikora, "Introduction to MPEG-7: multimedia content description interface", pp. 261-281, John Wiley & Sons, West Sussex, England, 2002.
  11. Ilaria Bartolini, Paola Ciaccia, and Marco Patella. "WARP: Accurate Retrieval of Shapes Using Phase of Fourier Descriptors and Time Warping Distance." IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 1, pp. 142-147 (2005). https://doi.org/10.1109/TPAMI.2005.21
  12. Xiaolong Dai, S. Khorram, "A feature-based image registration algorithm using improved chain-code representation combined with invariant moments", IEEE Transactions on Geoscience and Remote Sensing, Vol. 37, No. 5, pp. 2351-2362, 1999. https://doi.org/10.1109/36.789634
  13. A. Khotanzad, Y.H. Hong, "Invariant image recognition by Zernike moments", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, No. 5, pp. 489-497, 1990. https://doi.org/10.1109/34.55109
  14. Jerome Revaud, Guillaume Lavoue, Atilla Baskurt, "Improving Zernike Moments Comparison for Optimal Similarity and Rotation Angle Retrieval", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, No. 4, pp. 627-636, 2009. https://doi.org/10.1109/TPAMI.2008.115
  15. W. Y. Kim and Y. S. Kim, "A new region-based shape descirptor: The ART (Angular Radial Transform) Descriptor," ISO/IEC MPEG99/M5472, Maui, Dec. 1999.
  16. Junchul Chun, Dongsun Kim, "A Contents-based Drug Image Retrieval System Using Shape Classification and Color Information", Journal of Internet Computing and Services, Vol. 12, No. 6, pp. 117-128, 2011.
  17. Dong-Woo Kim, Young-Jun Song, Young-Gil Kim, Jae-Hyeong Ahn, "Content-Based Image Retrieval using Region Feature Vector", The KIPS Transactions : Part B, Vol. 13, No. 1, pp. 47-52, 2006.
  18. Minseok Choi, "Movement Search in Video Stream Using Shape Sequence", Journal of Korea Multimedia Society, Vol. 12, No. 4, pp. 492-501, 2009.