Significance of processing chrominance information for scene classification: a review

  • V. SowmyaEmail author
  • D. Govind
  • K. P. Soman


The primary objective of this paper is to provide a detailed review of various works showing the role of processing chrominance information for color-to-grayscale conversion. The usefulness of perceptually improved color-to-grayscale converted images for scene classification is then studied as a part of this presented work. Various issues identified for the color-to-grayscale conversion and improved scene classification are presented in this paper. The review provided in this paper includes, review on existing feature extraction techniques for scene classification, various existing scene classification systems, different methods available in the literature for color-to-grayscale image conversion, benchmark datasets for scene classification and color-to-gray-scale image conversion, subjective evaluation and objective quality assessments for image decolorization. In the present work, a scene classification system is proposed using the pre-trained convolutional neural network and Support Vector Machines developed utilizing the grayscale images converted by the image decolorization methods. The experimental analysis on Oliva Torralba scene dataset shows that the color-to-grayscale image conversion technique has a positive impact on the performance of scene classification systems.


Image decolorization Scene classification Color-to-grayscale Image quality 



  1. Akbas E, Ahuja N (2010) Low-level image segmentation based scene classification. In: Proc. IEEE int. conf. on pattern recognition (ICPR), pp 3623–3626Google Scholar
  2. Alsam A, Drew MS (2009) Fast multispectral2gray. J Imaging Sci Technol 53(6):1–19Google Scholar
  3. Ancuti CO, Ancuti C, Bekaert P (2011) Enhancing by saliency-guided decolorization. In: Proc. IEEE conf. computer vision and pattern recognition (CVPR), pp 257 – 264Google Scholar
  4. Bala R, Eschbach R (2004) Spatial color-to-grayscale transform preserving chrominance edge information. In: Proc. IEEE int. conf. on pattern recognition (ICPR), pp 82–86Google Scholar
  5. Bay H, Tuytelaars T, Gool LV (2008) Speeded-up robust features (surf). Pattern Recognit 110(3):346–359Google Scholar
  6. Bosch A, Munoz X, Zisserman A (2008) Scene classification using a hybrid generative/discriminative approach. IEEE Trans Pattern Anal Mach Intell (PAMI) 30(4):712–27Google Scholar
  7. Bosch A, Zisserman A, Munoz X (2006) Scene classification via plsa. In: Proc. ECCV, LNCS, pp 517–530Google Scholar
  8. Cadik M (2008) Perceptual evaluation of color-to-grayscale image conversions. Comput. Graphics Forum 27(7):1745–1754Google Scholar
  9. Dixit M, Rasiwasia N, Vasconcelos N (2011) Adapted gaussian models for image classification. In: Proc. int. conf. on computer vision and pattern recognition (CVPR), pp 937–943Google Scholar
  10. Dong G, Xie M (2005) Color clustering and learning for iamge segmentation based on neural networks. IEEE Trans Neural Netw 16(1):925–936Google Scholar
  11. Douglas R, Thomas Q, Robert D (2000) Speaker verification using adapted gaussian mixture models. Int J Digital Signal Process 10(1):19–41Google Scholar
  12. Faroudja YC (1988) NTSC and beyond. IEEE Trans Consum Electron 34(1):166–178Google Scholar
  13. Gooch AA, Olsen SC, Tumblin J, Gooch B (2005) Color2gray: salience-preserving color removal. ACM Trans Graphics (TOG) 24(3):634–639Google Scholar
  14. Grauman K, Darrell T (2005) Pyrmaid match kernels: discriminative classification with sets of image features. In: Proc. IEEE int. conf. on computer vision (ICCV), pp 1–8Google Scholar
  15. Grundland M, Dodgson NA (2007) Decolorize: fast, contrast enhancing, color to grayscale conversion. Int J Pattern Recognit 40(11):2891–2896Google Scholar
  16. Gunes A, Kalkan H, Durmus E (2016) Optimizing the color-to-grayscale conversion for image classification. Int J Signal Image Video Process 10(5):853–860. Google Scholar
  17. Guo Z, Zhang D (2010) A completed modeling of local binary pattern operator for texture classification. IEEE Trans Image Process 19(6):1657–1663MathSciNetzbMATHGoogle Scholar
  18. Hadjidemetriou E, Grossberg MD, Nayar SK (2004) Multiresolution histograms and their use in recognition. IEEE Trans Pattern Anal Mach Intell (PAMI) 26(7):831–847Google Scholar
  19. Horiuchi T, Nohara F, Tominaga S (2010) Accurate reversible color-to-gray mapping algorithm without distortion conditions. Pattern Recognit Lett 31(1):2405–2414Google Scholar
  20. Hua X (2012) Human computer interactions for converting color images to gray. Int J Neurocomputing 85(1):1–5Google Scholar
  21. Ionescu RT, Popescu M (2015) Have a snak. encoding spatial information with the spatial non-alignment kernel. In: Int. conf. on image analysis and processing (ICIAP), pp 97–108Google Scholar
  22. Ionescu RT, Popescu AL, Popescu D (2015) Texture classification with patch autocorrelation features. In: Proc. int.conf. on neural information processing (ICONIP), pp 1–11Google Scholar
  23. Ionescu RT, Ionescu AL, Mothe J, Popescu D (2018) Patch autocorrelation features: a translation and rotation invariant approach for image classification. Artif Intell Rev 49(4):549–580Google Scholar
  24. Ji Z, Fang M, Wang Y, Ma W (2016) Efficient decolorization preserving dominant distinctions. Visual Comput 32(12):1621–1631Google Scholar
  25. Kadir T, Brady M (2001) Scale, saliency and image description. Int J Comput Vis (IJCV) 45(2):83–105zbMATHGoogle Scholar
  26. Kanan C, Cottrell G (2012) Color-to-grayscale: Does the method matter in image recognition? PLoS ONE 7(1):1–7Google Scholar
  27. Kede M, Tiesong Z, Kai Z, Zhou W (2015) Objective quality assessment for color-to-gray image conversion. IEEE Trans Image Process 24(12):4673–4685MathSciNetGoogle Scholar
  28. Koenderink J, Doorn AV (1999) The structure of locally orderless images. Int J Comput Vis (IJCV) 31(2):159–168Google Scholar
  29. Krapac J, Verbeek J, Jurie F (2011) Modeling spatial layout with fisher vectors for image categorization. In: Proc. IEEE int. conf. on computer vision (ICCV), pp 1487–1494Google Scholar
  30. Li FF, Pietro P (2005) A bayesian hierarchical model for learning natural scene categories. In: Proc. int. conf. on computer vision and pattern recognition, (CVPR), pp 524–531Google Scholar
  31. Li Z, Liu G, Yang Y, You J (2012) Scale- and rotation-invariant local binary pattern using scale-adaptive texton and subuniform-based circular shift. IEEE Trans Image Process 21(4):2130–2140MathSciNetzbMATHGoogle Scholar
  32. Lim WH, Isa NAM (2011) Color to grayscale conversion based on neighborhood pixels effect approach for digital image. In: Proc. int. conf. on electrical and electronics engineering, pp 157–161Google Scholar
  33. Lissner I (2013) Image-difference prediction: from grayscale to color. IEEE Trans on Image Process 22(6):435–446MathSciNetzbMATHGoogle Scholar
  34. Liu CW, Liu TL (2013) A sparse linear model for saliency-guided decolorization. In: Proc. twentieth IEEE int. conf. image processing (ICIP), pp 1105 – 1109Google Scholar
  35. Liu Q, Xiong J, ZhuMinghui L, Wang Z (2017) Extended rgb2gray conversion model for efficient contrast preserving decolorization. Multimed Tools Appl 76(12):14055–14074Google Scholar
  36. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis (IJCV) 60(2):91–110MathSciNetGoogle Scholar
  37. Lu C, Li X, Jia J (2012a) Real-time contrast preserving decolorization. In: Proc. int. conf. comput. graphics and interactive techniques (SIGGRAPH), pp 34:1–34:4Google Scholar
  38. Lu C, Xu L, Jia J (2012b) Contrast preserving decolorization. In: Proc. IEEE int. conf. computational photography (ICCP), pp 1–7Google Scholar
  39. Lu C, Xu L, Jia J (2014) Contrast preserving decolorization with perception-based quality metrics. Int J Comput Vis 110(2):222–239Google Scholar
  40. Mantiuk R, Myszkowskia K, Seidel HP (2006) A perceptual framework for contrast processing of high dynamic range images. ACM Trans Appl Percept 3(3):286–308Google Scholar
  41. Menesatti P, Angelini C, Pallottino F, Antonucci F, Aguzzi J, Costa C (2012) Rgb color calibration for quantitative image analysis: the 3d thin-plate spline warping approach. IEEE Sensors 12(1):7063–7079Google Scholar
  42. Mikolajczyk K, Schmid C (2005) A performance evaluation of local descriptors. IEEE Trans Pattern Anal Mach Intell (PAMI) 27(10):1615–1630Google Scholar
  43. Morikawa S, Shibata T (2012) Scene image recognition based on the sequence of local image vectors represented by oriented edges. In: Proc. IEEE int. conf. on acoustics, speech, and signal processing, pp 1313–1316Google Scholar
  44. Neumann L, Cadik M, Nemcsics A (2007) An efficient perception-based adaptive color to gray transformation. In: Proc. third eurographics conf. computational aesthetics in graphics, visualization and imaging, pp 73–80Google Scholar
  45. Ojala T, Pietikainen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Int J. Comput Vis Image Underst 29(1):51–59Google Scholar
  46. Oliva A, Torralba A (2001) Modelling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis (IJCV) 42(3):145–175zbMATHGoogle Scholar
  47. Pedram M, Abbas EM, Shahram S (2014) Subjective and objective quality assessment of image: a survey. Majlesi J Electr Eng 9(1):55–83Google Scholar
  48. Qian X, Hua XS, Chen P, Ke L (2011) Plbp: an effective local binary patterns texture descriptor with pyramid representation. Pattern Recognit 44(10–11):2502–2515Google Scholar
  49. Queiroz RLD, Braun KM (2006) Color to gray and back: color embedding into textured gray images. IEEE Trans Image Process 15(6):1464–1470Google Scholar
  50. Rajan S, Sowmya V, Govind D, Soman KP (2017) Dependency of various color and intensity planes on cnn based image classification. In: Proc. third international symposium on signal processing and intelligent recognition systems (SIRS), pp 167–177.
  51. Rangayyan RM, Acha B, Serrano C (2011) Color image processing with biomedical applications. In: SPIEGoogle Scholar
  52. Rasche K, Geist R, Westall J (2005) Re-coloring images for gamuts of lower dimension. Int J Comput Graphics Forum 24(3):423–432Google Scholar
  53. Renninge LW, Malik J (2003) When is scene recognition just texture recognition? Int J Vis Res 44(1):2301–2311Google Scholar
  54. Serrano N, Savakis A, Luo J (2004) Improved scene classification using efficient low-level features and semantic cues. Pattern Recognit 37(9):1773–1784zbMATHGoogle Scholar
  55. Smith K, Landes PE, Thollot J, Myszkowski K (2008) Apparent greyscale: a simple and fast conversion to perceptually accurate images and video. Int J Comput Graphics Forum 27(2):193–200Google Scholar
  56. Sowmya V, Ajay A, Govind D, Soman KP (2017a) Improved color scene classification systemusing deep belief networks and support vector machines. In: Proc. IEEE int. conf. on signal and image processing applications (ICSIPA)Google Scholar
  57. Sowmya V, Govind D, Soman KP (2017b) Significance of contrast and structure features for an improved color image classification system. In: Proc. IEEE int. conf. on signal and image processing applications (ICSIPA)Google Scholar
  58. Sowmya V, Govind D, Soman KP (2017c) Significance of incorporating chrominance information for effective color-to-grayscale image conversion. Int J Signal Image Video Process 11(1):129–136. Google Scholar
  59. Suhre A, Kose K, Cetin AE, Gurcan MN (2010) Content-adaptive color transform for image compression. In: Proc. seventeenth int. conf. image processing, pp 189–192Google Scholar
  60. Svetlana L, Cordelia S, Jean P (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proc. int. conf. on computer vision and pattern recognition (CVPR), pp 2169–2178Google Scholar
  61. Vandenbroucke N, Macaire L, Postaire J (2003) Color image segementation by pixel classification in an adpated hybrid color space: application to soccer image analysis. Comput Vis Image Underst 90(1):190–216Google Scholar
  62. Viswanathan S, Divakaran G, Soman KP (2017) Significance of perceptually relevant image decolorization for scene classification. J Electron Imaging SPIE 26(6):129–136Google Scholar
  63. Vogel J, Schiele B (2007) Semantic modelling of natural scenes for content-based image retrieval. Int J Comput Vis (IJCV) 72(2):133–157Google Scholar
  64. Wallraven C, Caputo B, Graf A (2003) Recognition with local features: the kernel recipe. In: Proc. IEEE int. conf. on computer vision (ICCV), pp 257–264Google Scholar
  65. Wang Z (2011) Applications of objective image quality assessment methods. IEEE Signal Process Mag 28(6):137–142Google Scholar
  66. Wang Z, Bovik AC (2009) Mean sqaured error: Love it or leave it? A new look at signal fidelity measures. IEEE Signal Process Mag 26(1):98–117Google Scholar
  67. Wang Z, Bovik AC (2011) Reduced- and no-reference image quality assessment. IEEE Signal Process Mag 28(6):29–40Google Scholar
  68. Wang Z et al (2004) Image quality assessment: From error visibility to strcuture similarity. IEEE Trans. Image Processing 13(4):600–612MathSciNetGoogle Scholar
  69. Wang J, Yang J, Yu K, Lv F, Huang T, Gong Y (2010) Locality-constrained linear coding for image classification. In: Proc. IEEE int. conf. on computer vision and pattern recognition (CVPR), pp 3360–3367Google Scholar
  70. Wang L, Guo S, Huang W, Xiong Y, Qiao Y (2017) Knowledge guided disambiguation for large-scale scene classification with multi-resolution cnns. IEEE Trans Image Process 26(4):2055–2068MathSciNetGoogle Scholar
  71. Willamowski J, Arregui D, Csurka G, Dance CR, Fan L (2004) Categorizing nine visual classes using local appearance descriptors. In: Proc. ICPR workshop on learning for adaptable visual systems, pp 1–11Google Scholar
  72. Wu D, Sun DW (2013) Colour measurements by computer vision for food quality control. Trends Food Sci Technol 29(1):5–20Google Scholar
  73. Wu T, Toet A (2014) Color-to-grayscale conversion through weighted multiresolution channel fusion. J Electron Imaging 23(4):1–6Google Scholar
  74. Xie Z, Ling R, Wu K, Gao J (2012a) Learning robust independent bases for accurate scene categorization. In: Proc. IEEE int. conf. on image and signal processing (CISP), pp 459–463Google Scholar
  75. Xie Z, Ling R, Wu K, Gao J (2012b) Learning robust independent bases for accurate scene categorization. In: Proc. int. congress on image and signal processing (CISP), pp 459–463Google Scholar
  76. Xia J, Ehinger KA, Hays J, Torralba A, Oliva A (2016) Sun database: exploring a large collection of scene categories. Int J Comput Vis (IJCV) 119(1):3–22MathSciNetGoogle Scholar
  77. Xue W, Lam PS, Abdesselam B (2016) Visual descriptors for scene categorization: experimental evaluation. Artif Intell Rev 45(3):333–368Google Scholar
  78. Yang J, Yu K, Gong Y, Huang T (2009) Linear spatial pyramid matching using sparse coding for image classification. In: Proc. IEEE int. conf. on computer vision and pattern recognition (CVPR), pp 1794–1801Google Scholar
  79. Zhang W, Deng H, Dietterich TG, Mortensen EN (2006) A hierarchical object recognition system based on multi-scale principal curvature regions. In: Proc. eighteenth int. conf. pattern recognition (ICPR), pp 778–782Google Scholar
  80. Zhou B, Lapedriza A, Xiao J, Torralba A, Oliva A (2014) Learning deep features for scene recognition using places database. Adv Neural Inf Process Syst 1(1):487–495Google Scholar
  81. Zhou B, Khosla A, Lapedriza A, Torralba A, Oliva A (2016) Places: an image database for deep scene understanding. arXiv preprint arXiv:1610.02055

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© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Centre for Computational Engineering and Networking (CEN), Amrita School of EngineeringAmrita Vishwa VidyapeethamCoimbatoreIndia

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