Introduction to Image Color Feature

  • Jyotismita ChakiEmail author
  • Nilanjan Dey
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)


Two main factors motivate the need for color in image processing. First, color is a strong descriptor frequently simplifying the recognition and extraction of objects from a picture. Second, people can distinguish thousands of tones of color and intensity comparable to just about two dozen tones of gray [1, 2, 3, 4].


  1. 1.
    Rama Varior R, Wang G, Lu J, Liu T (2016) Learning invariant color features for person reidentification. IEEE Trans Image Process 25(7):3395–3410.
  2. 2.
    Benitez-Quiroz F, Srinivasan R, Martinez AM, Discriminant functional learning of color features for the recognition of facial action units and their intensities. IEEE Trans Pattern Anal Mach Intell.
  3. 3.
    Tonmoy TH, Hanif MA, Rahman HA, Khandaker N, Hossain I (2016) Error reduction in arsenic detection through color spectrum analysis. In: 2016 19th international conference on computer and information technology (ICCIT). Dhaka, pp 343–350.
  4. 4.
    Dey N, Ashour AS, Hassanien AE (2017) Feature detectors and descriptors generations with numerous images and video applications: a recap. In: Feature detectors and motion detection in video processing, pp 36–65.
  5. 5.
    Wang C, Li Z, Dey N, Li Z, Ashour AS, Fong SJ, Shi F (2018) Histogram of oriented gradient based plantar pressure image feature extraction and classification employing fuzzy support vector machine. J Med Imaging Health Inform 8(4):842–854.
  6. 6.
    Pi JK, Yang J, Zhong Q, Wu MB, Yang HC, Schwartzkopf M, Roth SV, Muller-Buschbaum P, Xu ZK (2019) Dual-layer nanofilms via mussel-inspiration and silication for non-iridescent structural color spectrum in flexible displays. ACS Appl Nano Mater. Scholar
  7. 7.
    Devlin RC, Khorasaninejad M, Chen WT, Oh J, Capasso F (2016) Broadband high-efficiency dielectric metasurfaces for the visible spectrum. Proc Natl Acad Sci 113(38):10473–10478. Scholar
  8. 8.
    Morley CV, Fortney JJ, Marley MS, Zahnle K, Line M, Kempton E, Lewis N, Cahoy K (2015) Thermal emission and reflected light spectra of super Earths with flat transmission spectra. Astrophys J 815(2):110CrossRefGoogle Scholar
  9. 9.
    Perlman I (2016) Absorption, light, spectra of for visual pigments. Encyclopedia of Ophthalmology, pp 1–2.
  10. 10.
    Wang S et al (2015) Micro-expression recognition using color spaces. IEEE Trans Image Process 24(12):6034–6047. Scholar
  11. 11.
    Fedenko VS, Shemet SA, Landi M (2017) UV–vis spectroscopy and colorimetric models for detecting anthocyanin-metal complexes in plants: an overview of in vitro and in vivo techniques. J Plant Physiol 212:13–28. Scholar
  12. 12.
    Cyriac P, Bertalmio M, Kane D, Vazquez-Corral J (2015) A tone mapping operator based on neural and psychophysical models of visual perception. In: Human vision and electronic imaging. International Society for Optics and Photonics, vol 9394, p 93941I.
  13. 13.
    Gao S, Yang K, Li C, Li Y (2015) Color constancy using double-opponency. IEEE Trans Pattern Anal Mach Intell 37(10):1973–1985.
  14. 14.
    Ganasala P, Kumar V, Prasad AD (2016) Performance evaluation of color models in the fusion of functional and anatomical images. J Med Syst 40(5):122.
  15. 15.
    Ganesan P, Sathish BS, Vasanth K, Sivakumar VG, Vadivel M, Ravi CN (2019) A comprehensive review of the impact of color space on image segmentation. In: 2019 5th international conference on advanced computing & communication systems (ICACCS). Coimbatore, India, pp 962–967.
  16. 16.
    Ganesan P, Sajiv G (2017) User oriented color space for satellite image segmentation using fuzzy based techniques. In: 2017 international conference on innovations in information, embedded and communication systems (ICIIECS). Coimbatore, pp 1–6.
  17. 17.
    Zhang Z, Huang W, Li W, Tian J (2017) Illumination-based and device-independent imaging model and spectral response functions. In: 2017 IEEE 7th annual international conference on cyber technology in automation, control, and intelligent systems (CYBER). Honolulu, HI, pp 47–52.
  18. 18.
    Vaishnavi D, Subashini TS (2015) Robust and invisible image watermarking in RGB color space using SVD. Procedia Comput Sci 46:1770–1777.
  19. 19.
    Kolkur S, Kalbande D, Shimpi P, Bapat C, Jatakia J (2017) Human skin detection using RGB, HSV and YCbCr color models. arXiv preprint arXiv:1708.02694
  20. 20.
    Bao X, Song W, Liu S (2017) Research on color space conversion model from CMYK to CIE-LAB based on GRNN. In: Pacific-Rim symposium on image and video technology. Springer, Cham, pp 252–261.
  21. 21.
    Shaik KB, Ganesan P, Kalist V, Sathish BS, Jenitha JMM (2015) Comparative study of skin color detection and segmentation in HSV and YCbCr color space. Procedia Comput Sci 57:41–48. Scholar
  22. 22.
    Saravanan G, Yamuna G, Nandhini S (2016) Real time implementation of RGB to HSV/HSI/HSL and its reverse color space models. In: 2016 international conference on communication and signal processing (ICCSP). Melmaruvathur, pp 0462–0466.
  23. 23.
    Ma J, Fan X, Yang SX, Zhang X, Zhu X (2018) Contrast limited adaptive histogram equalization-based fusion in YIQ and HSI color spaces for underwater image enhancement. Int J Pattern Recognit Artif Intell 32(07):1854018. Scholar
  24. 24.
    Prema CE, Vinsley SS, Suresh S (2016) Multi feature analysis of smoke in YUV color space for early forest fire detection. Fire Technol 52(5):1319–1342. Scholar
  25. 25.
    del Mar Pérez M, Ghinea R, Rivas MJ, Yebra A, Ionescu AM, Paravina RD, Herrera LJ (2016) Development of a customized whiteness index for dentistry based on CIELAB color space. Dental Mater 32(3):461–467.
  26. 26.
    Paramei GV, Griber YA, Mylonas D (2018) An online color naming experiment in Russian using Munsell color samples. Color Res Appl 43(3):358–374. Scholar
  27. 27.
    Gong J, Guo J (2016) Image copy-move forgery detection using SURF in opponent color space. Trans Tianjin Univ 22(2):151–157. Scholar
  28. 28.
    Sağ T, Çunkaş M (2015) Color image segmentation based on multiobjective artificial bee colony optimization. Appl Soft Comput 34:389–401. Scholar
  29. 29.
    Valenzuela G, Celebi ME, Schaefer G (2018) Color quantization using Coreset sampling. In: 2018 IEEE international conference on systems, man, and cybernetics (SMC). Miyazaki, Japan, pp 2096–2101.
  30. 30.
    Jiang N, Wu W, Wang L, Zhao N (2015) Quantum image pseudocolor coding based on the density-stratified method. Quantum Inf Process 14(5):1735–1755. Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.School of Information Technology and EngineeringVellore Institute of TechnologyVelloreIndia
  2. 2.Department of Information TechnologyTechno India College of TechnologyKolkataIndia

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