JACIII Vol.20 No.3 pp. 393-401
doi: 10.20965/jaciii.2016.p0393


FHSI: Toward More Human-Consistent Color Representation

Pakizar Shamoi*, Atsushi Inoue**, and Hiroharu Kawanaka***

*Department of Information Systems Management, Kazakh-British Technical University
Almaty, Kazakhstan
**Department of Computer Science, Eastern Washington University
Washington, USA
***Graduate School of Engineering, Mie University
1577 Kurima-machiya, Tsu, Mie 514-7507, Japan

May 23, 2015
December 7, 2015
Online released:
May 19, 2016
May 19, 2016
HSI color model, fuzzy sets, perceptual color space, apparel coordination, image retrieval

In this paper, we propose a novel approach toward the development of a perceptual color space, FHSI, which stands for “Fuzzy HSI,” because it is based on the fuzzification of the well-known HSI color space. FHSI represents a set of fuzzy colors obtained by partitioning the gamut of feasible colors in the HSI model corresponding to standardized linguistic tags. In fact, color categorization was performed on the basis of personal judgments of humans collected by way of an online survey. This approach helps to significantly enhance color matching and similarity searches by producing more intuitive and human-consistent output for users. The introduced method has potential for use in various color image applications involving query processing, for example, in the coordination of online apparel shopping.

  1. [1] J. Delon, A. Desolneux, J-L. Lisani, and A-B. Petro, “Automatic color palette,” Inverse Problems and Imaging (IPI), Vol.1, No.2, pp. 265-287, May 2007.
  2. [2] Napoleon H. Reyes and Elmer P. Dadios, “Dynamic Color Object Recognition Using Fuzzy Logic,” J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.8, No.1, 2004.
  3. [3] M. Rahmat Widyanto and Tatik Maftukhah, “Fuzzy Relevance Feedback in Image Retrieval for Color Feature Using Query Vector Modification Method,” J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.14, No.1, 2010.
  4. [4] R. Schettini, G. Ciocca and S. Zuffi, “A survey on methods for colour image indexing and retrieval in image databases,” Color Imaging Science: Exploiting Digital Media, (R. Luo, L. MacDonald eds.), J. Wiley, pp. 183-211, 2002.
  5. [5] K. Plataniotis and A. Venetsanopoulos, “Color Image Processing and Applications,” Springer Science & Business Media, 2000 .
  6. [6] P. Shamoi, A. Inoue, H. Kawanaka, “Fuzzy Color Space for Apparel Coordination,” Open J. of Information Systems (OJIS), Vol.1, No.2, pp. 20-28, 2014.
  7. [7] J. Chamorro-Martínez, D. Sánchez, J. M. Soto-Hidalgo and P. Martínez-Jiménez, “Histograms for Fuzzy Color Spaces,” Advances in Intelligent and Soft Computing, Vol.107, pp. 339-350, 2012.
  8. [8] J. Chamorro-Martínez, D. Sánchez and J. M. Soto-Hidalgo, “A Novel Histogram Definition for Fuzzy Color Spaces,” IEEE Int. Conf. on Fuzzy Systems, pp. 2149-2156, 2008.
  9. [9] M. Nachtegaela, D. Vander Weken, V. De Witte, S. Schulte, T. Mélange, and E. E. Kerre, “Color Image Retrieval using Fuzzy Similarity Measures and Fuzzy Partitions,” IEEE Int. Conf. on Image Processing, Vol.6, pp. 13-16, 2007.
  10. [10] A. Younes, “Color Image Profiling Using Fuzzy Sets,” Turkish J. of Electrical Engineering and Computer Science, Vol.13, Issue 3, 2005.
  11. [11] T. Hansen, S. Walker, and K. Gegenfurtner, “Effects of spatial and temporal context on color categories and color constancy,” J. of Vision, Vol.7, No.4:(2), pp. 1-5, 2007.
  12. [12] Sugano Naotoshi, “Fuzzy Set Theoretical Approach to the RGB Triangular System,” J. of Japan Society for Fuzzy Theory and Intelligent Informatics, Vol.19, No.1, 2007.
  13. [13] E. Broek, Th. Schouten and P. Kisters, “Modeling human color categorization,” Pattern Recognition Letters, Vol.29, pp. 1136-144, 2008.
  14. [14] E. Blotta, A. Bouchet, V. Ballarin, and J. Pastore, “Enhancement of medical images in HSI color space,” J. of Physics: Conf. Series 332, 2011.
  15. [15] C. Fatichah, M. L. Tangel, M. R. Widyanto, F. Dong, and K. Hirota, “Interest-Based Ordering for Fuzzy Morphology on White Blood Cell Image Segmentation,” J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.16, No.1, 2012.
  16. [16] Y. Li-Jie, L. De-Sheng, and Z. Guan-Ling, “Automatic Image Segmentation Based on Human Color Perceptions,” I. J. Image, Graphics and Signal Processing, Vol.1, 2009.
  17. [17] X. Yanhui, W. Zhengyou, W. Jin, and W. Zheng, “Color Distortion of Digital Image and its Detection,” TELKOMNIKA, Vol.11, No.8, pp. 65-71, August 2013.
  18. [18] T. Regier and P. Kay, “Language, thought, and color: Whorf was half right,” Trends in Cognitive Sciences, Vol.13 No.10, 2009.
  19. [19] J. Cunningham, “Determining an optimal membership function based on community consensus in a fuzzy database system,” ACM Southeast Regional Conf., Vol.632, ACM Press, 2006.
  20. [20] C. Zhang and P. Wang, “A new method of color image segmentation based on intensity and hue clustering,” Pattern Recognition, 2000.
  21. [21] M. Tobar, C. Platero, P. González and G. Asensio, “Mathematical Morphology in the HSI Colour Space,” Pattern Recognition and Image Analysis, IPRIA, June, 2007.
  22. [22] S. Xu, C. Li, S. Jiang and X. Liu, “Similarity measures for content-based image retrieval based on intuitionistic fuzzy set theory,” J. of Computers, Vol.7, No.7, DOI: 10.4304/jcp.7.7.1733-1742, 2012.
  23. [23] M. Nachtegael, S. Schulte, V. De Witte, T. Mélange and E. E. Kerre, “Image Similarity – From Fuzzy Sets to Color Image Applications,” Advances in Visual Information Systems, Vol.4781 of the series Lecture Notes in Computer Science, pp. 26-37, 2007.

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