Journal of Integrative Natural Science (통합자연과학논문집)

The Basic Science Institute Chosun University (조선대학교 기초과학연구원)
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Technique According to the Calculation of Thresholds of Histogram Based on Overlap Areas for Reducing

  • An, Young-Eun (National Program of Excellence in Software center, Chosun University) ;
  • Bae, Sang-Hyun (Department of Computer Science & Statistics, Chosun University) ;
  • Kim, Tae-Yeun (National Program of Excellence in Software center, Chosun University)
  • Received : 2020.06.11
  • Accepted : 2020.06.17
  • Published : 2020.06.30
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Abstract

In In this study, technique has been suggested according to the calculation of thresholds of histogram based on overlap areas for reducing noise while analyzing the functions of them. Suggested algorithm is to convert histogram extracted from color images to gray level and select overlap areas from extracted histogram. In addition, feature table is configured after extracting histogram in the relevant overlap area while comparing and retrieving for query and database video images by using this feature table. Suggested retrieval system has been confirmed to be more outstanding with retrieval function in video images with more noises than the system that only used color histogram.

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References

  1. M. Flicker, et al. "Query by image and video content: The QBIC system," IEEE Compuer magazine, 28(9): 23-32, 2005.
  2. A. K. Jain and A. Vailaya, "Image retrieval using color and shape," Pattern Recognition, vol. 29, No. 8, pp. 1233-1244, 2006. https://doi.org/10.1016/0031-3203(95)00160-3
  3. Arnold W.M. Smeulders, Marcel Worring, Simone Santini, Amarnath Gupta, and Ramesh Jain, "Content-based image retrieval at the end of the early years," IEEE Transactions of Pattern Analysis and Machine Intelligence, vol. 22, No. 12, pp. 1349-1380, December 2010.
  4. Yong Rui and Thomas S. Huang, "Image retrieval: Current technologies, promising directions, and open issues," Journal of Visual Communication and Image Representation, vol. 10, pp. 39-62, 2009. https://doi.org/10.1006/jvci.1999.0413
  5. Theo Gevers and Arnold W.M. Smeulders, "PicTo-Seek: Comqlsing color and shape invariant features for image retrieval," IEEE Transactions on Image Processing, vol. 9, No. 1, pp. 102-119, January 2011.
  6. G. Pass and R. Zabih, "Comparing images using joint histogram", Multimedia Systems, Vol.7, pp.234?240, 2009. https://doi.org/10.1007/s005300050125
  7. M. Carlotto, "Histogram analysis using a Scale-space approach," IEEE Transactions of Pattern Analysis and Machine Intelligence, vol. 9, no. 1, pp. 121-129, 1997.
  8. J. Hafner, H. Sawhney, W. Equitz, M. Flickner and W. Niblack, "Efficient color histogram indexing for quadratic form distance functions," IEEE Transactions of Pattern Analysis and Machine Intelligence, vol. 17, no. 7, pp. 729-736, July 2005.
  9. Y. Dai and Y. Nakano, "Extraction of Facial Images from Complex Background Using Color Information and SGLD Matrices", Proceedings of International Workshop on Automatic Face and Gesture-Recognition, Zurich, pp. 238-242, 2005.
  10. Y. Dai Y. Nakano, "Face-Texture Model Based on SGLD and Its Application in Face Detection a Color Scene", Pattern Recognition, Vol. 29, No. 6, pp. 1007-1017, 2006. https://doi.org/10.1016/0031-3203(95)00139-5
  11. R. C. Gonzalez and R. E. Woods, Digital Image Processing, Addison Wesley, 2003.