Content-based Image Retrieval Using Color Adjacency and Gradient

칼라 인접성과 기울기를 이용한 내용 기반 영상 검색

  • Jin, Hong-Yan (School of Electronic and Electrical Engineering, Kyungpook National University) ;
  • Lee, Ho-Young (School of Electronic and Electrical Engineering, Kyungpook National University) ;
  • Kim, Hee-Soo (School of Electronic and Electrical Engineering, Kyungpook National University) ;
  • Kim, Gi-Seok (School of Electronic and Electrical Engineering, Kyungpook National University) ;
  • Ha, Yeong-Ho (School of Computer and Electronic Engineering, Kyongju University)
  • Published : 2001.01.01

Abstract

A new content-based color image retrieval method integrating the features of the color adjacency and the gradient is proposed in this paper. As the most used feature of color image, color histogram has its own advantages that it is invariant to the changes in viewpoint and the rotation of the image etc., and the computation of the feature is simple and fast. However, it is difficult to distinguish those different images having similar color distributions using histogram-based image retrieval, because the color histogram is generated on uniformly quantized colors and the histogram itself contains no spatial information. And another shortcoming of the histogram-based image retrieval is the storage of the features is usually very large. In order to prevent the above drawbacks, the gradient that is the largest color difference of neighboring pixels is calculated in the proposed method instead of the uniform quantization which is commonly used at most histogram-based methods. And the color adjacency information which indicates major color composition feature of an image is extracted and represented as a binary form to reduce the amount of feature storage. The two features are integrated to allow the retrieval more robust to the changes of various external conditions.

본 논문에서는 칼라 인접성과 기울기를 이용한 새로운 내용 기반 영상 검색 방법을 제안한다. 칼라 영상의 특징 정보로 사용되는 칼라 히스토그램은 시점이나 영상의 회전등의 영향을 적게 받고 특징 정보의 계산이 간단하고 빠른 장점이 있지만 칼라의 위치 정보를 나타낼 수 없기 때문에 균일 양자화에 의해 비슷한 히스토그램을 가진 서로 다른 영상을 구별하지 못하고 특징 저장량이 많은 등 단점이 있다. 제안한 방법은 기존의 방법들에서 보편적으로 사용하는 양자화 대신 영상에서의 인접 화소의 칼라 변화량 즉 기울기를 계산하여 보다 정확한 색차를 구함으로써 비슷한 칼라가 서로 다르게 양자화됨으로 인한 오차를 감소시켰다. 동시에 영상의 주요 칼라 구성 특징을 나타나는 칼라 인접성 정보를 추출하여 이진 배열로 표시함으로써 특징 정보의 방대한 저장량을 줄이고 비교속도를 향상시켰다. 실험 결과 기존의 검색 방법에 비하여 제안한 방법은 적은 특징 저장 양으로 외부조건의 변화에 더욱 강건함을 보여주고 있다.

Keywords

References

  1. W. Niblack, R.Barber, W.Equitz, M.Flicker, E.Glasman, D.Petkovic, and P.Yanker, 'The QBIC Project : querying images by content using color, texture and shape,' Storage & Retrieval for Image and Video Databases, M.H.Loew, ed., Proc. SPIE 1908, 1993
  2. J. R. Smith and Shih Fu Chang, 'VisualSEEK : a fully automated content-based image query system,' ACM Multimedia conference, November 1996
  3. V. E. Ogle and M.Stonebraker, 'Chabot : Retrieval from a relational database of images,' IEEE Computer, 28(9), pp.40-48, 1995 https://doi.org/10.1109/2.410150
  4. Theo Gevers, and Arnold W. M. Smeulders, 'picToSeek : combining color and shape invariant features for image retrieval,' IEEE Transactions on Image Processing, vol.9, no.1, pp.102-119, Jan 2000 https://doi.org/10.1109/83.817602
  5. M. J. Swain and D.H. Ballard, 'Color indexing,' Int. J. Computer Vision, 7(1), pp.11-32, 1991 https://doi.org/10.1007/BF00130487
  6. Babu M. Mehtre, Mohan S. Kankanhalli, A. Desai Narasimhalu, and Guo Chang Man, 'Color matching for image retrieval,' Pattern Recognition Letters, 16, pp.325-331, Mar. 1995 https://doi.org/10.1016/0167-8655(94)00096-L
  7. M. K. Mandal, T. Aboulnasr, and S.Panchanathan, 'Image indexing using moments and wavelets,' IEEE Transactions on Consumer Electron 42(3), pp.557-565, 1996 https://doi.org/10.1109/30.536156
  8. J. Hafner, H. S. Sawhney, W.Equita, M.Flickner and W.Niblack, 'Efficient color histogram indexing for quadratic form distance functions,' IEEE Transactions on PAMI, 17(7), pp. 729-736, July 1995 https://doi.org/10.1109/34.391417
  9. W. Hsu, T. S. Chua, and H.K.Pung, 'An integrated color-spatial approach to content-based image retrieval,' ACM Multimedia Conference, pp.305-313, 1995
  10. M. Stricker and A. Dimai, 'Color indexing with weak spatial constraints,'SPIE proceedings 2670, pp.29-40, 1996 https://doi.org/10.1117/12.234802
  11. G. Pass, R. Zabih, 'Histogram refinement for content-based image retrieval,' IEEE Workshop on Application of Computer Vision, pp.96-102, 1996
  12. J. Huang, S. R. Kumar, M.Mitra, W. J. Zhu and R.Zabih, 'Image indexing using color correlograms,' Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, pp.762-768, June 1997
  13. I. K. Park, I. D. Yun, and S.U.Lee, 'Color image retrieval using hybrid graph representation,' Image and Vision Computing 17, pp.465-474, 1999 https://doi.org/10.1016/S0262-8856(98)00139-5
  14. J. Matas, R. Marik, and J.Kittler, 'On representation and matching of multi-colord objects,' Proceedings of IEEE International Conference on Computer Vision, pp.726-732, 1995