Color Invariants for Object Recognition



Color is a very important cue for object recognition, which can help increase the discriminative power of an object-recognition system and also make it more robust to variations in the lighting and imaging conditions. Nonetheless, even though most image acquisition devices provide color data, a lot of object-recognition systems rely solely on simple grayscale information. Part of the reason for this is that although color has advantages, it also introduces some complexities. In particular, the RGB values of a digital color image are only indirectly related to the surface “color” of an object, which depends not only on the object’s surface reflectance but also on such factors as the spectrum of the incident illumination, surface gloss, and the viewing angle. As a result, there has been a great deal of research into color invariants that encode color information but at the same time are insensitive to these other factors. This chapter describes these color invariants, their derivation, and their application to color-based object recognition in detail. Recognizing objects using a simple global image matching strategy is generally not very effective since usually an image will contain multiple objects, involve occlusions, or be captured from a different viewpoint or under different lighting conditions than the model image. As a result, most object-recognition systems describe the image content in terms of a set of local descriptors—SIFT, for example—that describe the regions around a set of detected keypoints. This chapter includes a discussion of the three color-related choices that need to be made when designing an object-recognition system for a particular application: Color-invariance, keypoint detection, and local description. Different object-recognition situations call for different classes of color invariants depending on the particular surface reflectance and lighting conditions that will be encountered. The choice of color invariants is important because there is a trade-off between invariance and discriminative power. All unnecessary invariance is likely to decrease the discriminative power of the system. Consequently, one part of this chapter describes the assumptions underlying the various color invariants, the invariants themselves, and their invariance properties. Then with these color invariants in hand, we turn to the ways in which they can be exploited to find more salient keypoints and to provide richer local region descriptors. Generally but not universally, color has been shown to improve the recognition rate of most object-recognition systems. One reason color improves the performance is that including it in keypoint detection increases the likelihood that the region surrounding the keypoint will contain useful information, so descriptors built around these keypoints tend to be more discriminative. Another reason is that color-invariant-based keypoint detection is more robust to variations in the illumination than grayscale-based keypoint detection. Yet another reason is that local region descriptors based on color invariants more richly characterize the regions, and are more stable relative to the imaging conditions, than their grayscale counterparts.


Color-based object recognition Color invariants Keypoint detection SIFT Local region descriptors Illumination invariance Viewpoint invariance Color ratios Shadow invariance. 


  1. 1.
    Abdel-Hakim A, Farag A (2006) Csift: A sift descriptor with color invariant characteristics. In: 2006 IEEE computer society conference on computer vision and pattern recognition, New York, USA, vol 2, pp 1978–1983Google Scholar
  2. 2.
    Alexe B, Deselaers T, Ferrari V (2010) What is an object? IEEE computer society conference on computer vision and pattern recognition 4:73–80CrossRefGoogle Scholar
  3. 3.
    Ancuti C, Bekaert P (2007) Sift-cch: Increasing the sift distinctness by color co-occurrence histograms. In: Proceedings of the 5th international symposium on image and signal processing and analysis, Istambul, Turkey, pp 130–135Google Scholar
  4. 4.
    Arulampalam M, Maskell S, Gordon N, Clapp T (2002) A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Trans Signal Process 50(2):174–188CrossRefGoogle Scholar
  5. 5.
    Barnard K, Martin L, Coath A, Funt B (2002) A comparison of computational color constancy algorithms. II. Experiments with image data. IEEE Trans Image Process 11(9):985–996Google Scholar
  6. 6.
  7. 7.
  8. 8.
    Bay H, Ess A, Tuytelaars T, Gool LV (2008) Surf: Speeded up robust features. Comput Vis Image Understand 110:346–359CrossRefGoogle Scholar
  9. 9.
    Beaudet PR (1978) Rotationally invariant image operators. In: Proceedings of the International Conference on Pattern Recognition, Kyoto, Japan, pp 579–583Google Scholar
  10. 10.
    Beckmann P, Spizzichino A (1987) The scattering of electromagnetic waves from rough surfaces, 2nd edn. Artech House Inc, Norwood, USAGoogle Scholar
  11. 11.
    Bosch A, Zisserman A, Munoz X (2006) Scene classification via plsa. In: Proceedings of the European conference on computer vision, Graz, Austria, pp 517–530Google Scholar
  12. 12.
    Burghouts G, Geusebroek JM (2009) Performance evaluation of local colour invariants. Comput Vis Image Understand 113(1):48–62CrossRefGoogle Scholar
  13. 13.
    Canny J (1986) Computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679–698CrossRefGoogle Scholar
  14. 14.
    Chang P, Krumm J (1999) Object recognition with color cooccurrence histograms. In: In IEEE conference on computer vision and pattern recognition (CVPR), vol 2, p 504Google Scholar
  15. 15.
    Chen X, Hu X, Shen X (2009) Spatial weighting for bag-of-visual-words and its application in content-based image retrieval. In: Advances in knowledge discovery and data mining, lecture notes in computer science, vol 5476, pp 867–874Google Scholar
  16. 16.
    Chu DM, Smeulders AWM (2010) Color invariant surf in discriminative object tracking. In: ECCV workshop on color and reflectance in imaging and computer vision, Heraklion, Crete, GreeceGoogle Scholar
  17. 17.
    Ciocca G, Marini D, Rizzi A, Schettini R, Zuffi S (2001) On pre-filtering with retinex in color image retrieval. In: Proceedings of the SPIE Conference on Internet Imaging II, San Jos, California, USA, vol 4311, pp 140–147Google Scholar
  18. 18.
    Dahl A, Aanaes H (2008) Effective image database search via dimensionality reduction. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshop, Anchorage, Alaska, pp 1–6Google Scholar
  19. 19.
    Dinet E, Kubicki E (2008) A selective attention model for predicting visual attractors. In: Proceedings of IEEE international conference on acoustics, speech, and signal processing, États-Unis, pp 697–700Google Scholar
  20. 20.
    Elsayad I, Martinet J, Urruty T, Djeraba C (2010) A new spatial weighting scheme for bag-of-visual-words. In: Proceedings of the international workshop on content-based multimedia indexing (CBMI 2010), Grenoble, France, pp 1 –6Google Scholar
  21. 21.
    Farag A, Abdel-Hakim A (2004) Detection, categorization and recognition of road signs for autonomous navigation. In: Proceedings of Advanced Concepts in Intelligent Vision Systems, Brussel, Belgium, pp 125–130Google Scholar
  22. 22.
    Felzenszwalb PF, Huttenlocher DP (2004) Efficient graph-based image segmentation. Int J Comput Vis 59:167–181CrossRefGoogle Scholar
  23. 23.
    Finlayson G, Hordley S (2001) Colour constancy at a pixel. J Opt Soc Am 18(2):253–264CrossRefGoogle Scholar
  24. 24.
    Finlayson GD, Trezzi E (2004) Shades of gray and colour constancy. In: Proceeding color imaging conference, Scottsdale, Arizona, pp 37–41Google Scholar
  25. 25.
    Finlayson G, Drew M, Funt B (1994) Color constancy : generalized diagonal transforms suffice. J Opt Soc Am 11(A):3011–3020Google Scholar
  26. 26.
    Finlayson GD, Drew MS, Funt BV (1994b) Spectral sharpening : sensor transformations for improved color constancy. J Opt Soc Am 11(A):1553–1563Google Scholar
  27. 27.
    Finlayson G, Chatterjee S, Funt B (1995) Color angle invariants for object recognition. In: Proceedings of the 3rd IS&T/SID color imaging conference, Scottsdale, Arizona, pp 44–47Google Scholar
  28. 28.
    Finlayson G, Schiele B, Crowley J (1998) Comprehensive colour image normalization. Lecture notes in computer science 1406:475–490. URL
  29. 29.
    Finlayson G, Hordley S, Hubel P (2001) Color by correlation: a simple, unifying framework for color constancy. IEEE Trans Pattern Anal Mach Intell 23(11):1209–1221CrossRefGoogle Scholar
  30. 30.
    Finlayson G, Drew M, Lu C (2004) Intrinsic images by entropy minimization. In: Proceedings of the European conference on computer vision, Prague, Czech Republic, pp 582–595Google Scholar
  31. 31.
    Finlayson G, Hordley S, Schaefer G, Tian GY (2005) Illuminant and device invariant colour using histogram equalisation. Pattern Recogn 38:179–190CrossRefGoogle Scholar
  32. 32.
    Forssén PE (2007) Maximally stable colour regions for recognition and matching. In: IEEE conference on computer vision and pattern recognition, IEEE computer society, IEEE, Minneapolis, USAGoogle Scholar
  33. 33.
    Forssén P, Moe A (2009) View matching with blob features. Image Vis Comput 27(1–2): 99–107CrossRefGoogle Scholar
  34. 34.
    Frangi A, Niessen W, Vincken K, Viergever M (1998) Multiscale vessel enhancement filtering. In: Proceeding of the MICCAI98 lecture notes in computer science, Berlin, vol 1496, pp 130–137Google Scholar
  35. 35.
    Funt B, Finlayson G (1995) Color constant color indexing. IEEE Trans Pattern Anal Mach Intell 17(5):522–529CrossRefGoogle Scholar
  36. 36.
    Funt B, Cardei VC, Barnard K (1999) Method of estimating chromaticity of illumination using neural networks. In: United States Patent, USA, vol 5,907,629Google Scholar
  37. 37.
    Gabriel P, Hayet JB, Piater J, Verly J (2005) Object tracking using color interest points. In: IEEE conference on advanced video and signal based surveillance, IEEE computer society, Los Alamitos, CA, USA, vol 0, pp 159–164Google Scholar
  38. 38.
    Gao K, Lin S, Zhang Y, Tang S, Ren H (2008) Attention model based sift keypoints filtration for image retrieval. In: Proceedings of seventh IEEE/ACIS international conference on computer and information science, Washington, DC, USA, pp 191–196Google Scholar
  39. 39.
    Geusebroek J (2000) Color and geometrical structure in images. PhD thesis, University of AmsterdamGoogle Scholar
  40. 40.
    Geusebroek J (2006) Compact object descriptors from local colour invariant histograms. In: British machine vision conference, vol 3, pp 1029–1038Google Scholar
  41. 41.
    Geusebroek JM, van den Boomgaard R, Smeulders AWM, Dev A (2000) Color and scale: the spatial structure of color images. In: Proceedings of the European conference on computer vision, Dublin, Ireland, pp 331–341Google Scholar
  42. 42.
    Geusebroek JM, van den Boomgaard R, Smeulders AWM, Geerts H (2001) Color invariance. IEEE Trans Pattern Anal Machine Intell 23(12):1338–1350CrossRefGoogle Scholar
  43. 43.
    Gevers T, Smeulders A (1999) Color-based object recognition. Pattern Recogn 32:453–464CrossRefGoogle Scholar
  44. 44.
    Gevers T, Stokman H (2004) Robust histogram construction from color invariants for object recognition. IEEE Trans Pattern Anal Mach Intell 23(11):113–118CrossRefGoogle Scholar
  45. 45.
    Goedem T, Tuytelaars T, Gool LV (2005) Omnidirectional sparse visual path following with occlusion-robust feature tracking. In: 6th workshop on omnidirectional vision, camera networks and non-classical cameras, OMNIVIS05, in Conjunction with ICCV 2005, Beijing, ChinaGoogle Scholar
  46. 46.
    Gouet V, Montesinos P, Deriche R, Pel D (2000) Evaluation de dtecteurs de points d’intrt pour la couleur. In: Proceeding congrs Francophone AFRIF-AFIA, Reconnaissance des Formes et Intelligence Artificielle, Paris, vol 2, pp 257–266Google Scholar
  47. 47.
    Hamilton Y, Gortler S, Zickler T (2008) A perception-based color space for illumination invariant image processing. In: Proceeding of the special interest group in GRAPHics (SIGGRAPH), Los Angeles, California, USA, vol 27, pp 1–7Google Scholar
  48. 48.
    Harris C, Stephens M (1988) A combined corner and edge detector. In: Proceedings of the 4th Alvey vision conference, Manchester, pp 147–151Google Scholar
  49. 49.
    Healey G, Slater D (1995) Global color contancy:recognition of objects by use of illumination invariant properties of color distributions. J Opt Soc Am 11(11):3003–3010CrossRefGoogle Scholar
  50. 50.
    Hegazy D, Denzler J (2008) Boosting colored local features for generic object recognition. Pattern Recogn Image Anal 18(2):323–327CrossRefGoogle Scholar
  51. 51.
    Heidemann G (2004) Focus-of-attention from local color symmetries. PAMI 26(7):817–830CrossRefGoogle Scholar
  52. 52.
    Heitger F, Rosenthaler L, von der Heydt R, Peterhans E, Kubler O (1992) Simulation of neural contour mechanisms: from simple to end-stopped cells. Vis Res 32(5):963–981CrossRefGoogle Scholar
  53. 53.
    Hou X, Zhang L (2007) Saliency detection: a spectral residual approach. IEEE computer society conference on computer vision and pattern recognition 0:1–8Google Scholar
  54. 54.
    Hu L, Jiang S, Huang Q, Gao W (2008) People re-detection using adaboost with sift and color correlogram. In: Proceedings of the IEEE international conference on image processing, San Diego, California, USA, pp 1348–1351Google Scholar
  55. 55.
    Huang J, Kumar SR, Mitra M, Zhu W, Zabih R (1997) Image indexing using color correlogram. IEEE conference on computer vision and pattern recognition pp 762–768Google Scholar
  56. 56.
    Inria database. URL
  57. 57.
    Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20(11):1254–1259CrossRefGoogle Scholar
  58. 58.
    (ITU) IRCC (1990) Basic parameter values for the hdtv standard for the studio and for international programme exchange. Tech. Rep. 709-2, CCIR RecommendationGoogle Scholar
  59. 59.
    Jost T, Ouerhani N, von Wartburg R, Muri R, Hugli H (2005) Assessing the contribution of color in visual attention. Comput Vis Image Understand 100:107–123CrossRefGoogle Scholar
  60. 60.
    Khan F, van de Weijer J, Vanrell M (2009) Top-down color attention for object recognition. In: Proceedings of the international conference on computer vision, Japan, pp 979–986Google Scholar
  61. 61.
    Klinker G, Shafer S, Kanade T (1991) A physical approach to color image understanding. Int J Comput Vis 4(1):7–38CrossRefGoogle Scholar
  62. 62.
    von Kries J (1970) Influence of adaptation on the effects produced by luminous stimuli. In: MacAdam, D.L. (ed) Sources of color vision. MIT Press, CambridgeGoogle Scholar
  63. 63.
    Kubelka P (1948) New contribution to the optics of intensity light-scattering materials, part i. J Opt Soc Am A 38(5):448–457MathSciNetCrossRefGoogle Scholar
  64. 64.
    Lambert JH (1760) Photometria sive de mensure de gratibus luminis, colorum umbrae. Eberhard KlettGoogle Scholar
  65. 65.
    Land E (1977) The retinex theory of color vision. Sci Am 237:108–129CrossRefGoogle Scholar
  66. 66.
    Land E (1986) An alternative technique for the computation of the designator in the retinex theory of color vision. In: Proceedings of the national academy science of the United State of America, vol 83, pp 3078–3080CrossRefGoogle Scholar
  67. 67.
    Lenz R, Tran L, Meer P (1999) Moment based normalization of color images. In: IEEE workshop on multimedia signal processing, Copenhagen, Denmark, pp 129–132Google Scholar
  68. 68.
    Li J, Allinson NM (2008) A comprehensive review of current local features for computer vision. Neurocomput 71(10-12):1771–1787. DOI 2007.11.032Google Scholar
  69. 69.
    Lindeberg T (1994) Scale-space theory in computer vision. Springer, London, UKGoogle Scholar
  70. 70.
    Locher P, Nodine C (1987) Symmetry catches the eye. Eye Movements: from physiology to cognition, North-Holland Press, AmsterdamGoogle Scholar
  71. 71.
    Logvinenko AD (2009) An object-color space. J Vis 9:1–23CrossRefGoogle Scholar
  72. 72.
    Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110CrossRefGoogle Scholar
  73. 73.
    Luke RH, Keller JM, Chamorro-Martinez J (2008) Extending the scale invariant feature transform descriptor into the color domain. Proc ICGST Int J Graph Vis Image Process, GVIP 08:35–43Google Scholar
  74. 74.
    Marques O, Mayron L, Borba G, Gamba H (2006) Using visual attention to extract regions of interest in the context of image retrieval. In: Proceedings of the 44th annual Southeast regional conference, ACM, ACM-SE 44, pp 638–643Google Scholar
  75. 75.
    Matas J, Chum O, Martin U, Pajdla T (2002) Robust wide baseline stereo from maximally stable extremal regions. In: Proceeding of the British machine vision conference, pp 384–393Google Scholar
  76. 76.
    Mikolajczyk K, Schmid C (2004) Scale & affine invariant interest point detectors. Int J Comput Vision 60:63–86Google Scholar
  77. 77.
    Mikolajczyk K, Schmid C (2005) A performance evaluation of local descriptors. IEEE Trans Pattern Anal Mach Intell 27:1615–1630CrossRefGoogle Scholar
  78. 78.
    Mikolajczyk K, Tuytelaars T, Schmid C, Zisserman A, Matas J, Schaffalitzky F, Kadir T, Gool LV (2005) A comparison of affine region detectors. Int J Comput Vis 65(1/2):43–72. URL Google Scholar
  79. 79.
    Mindru F, Moons T, van Gool L (1999) Recognizing color patterns irrespective of viewpoints and illuminations. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 368–373Google Scholar
  80. 80.
    Mindru F, Tuytelaars T, Gool LV, Moons T (2004) Moment invariants for recognition under changing viewpoint and illumination. Comput Vis Image Understand 1(3):3–27CrossRefGoogle Scholar
  81. 81.
    Ming A, Ma H (2007) A blob detector in color images. In: Proceedings of the 6th ACM international conference on image and video retrieval, ACM, New York, NY, USA, CIVR ’07, pp 364–370Google Scholar
  82. 82.
    Mollon J (2006) Monge: The verriest lecture, lyon, july 2005. Visual Neurosci 23:297–309CrossRefGoogle Scholar
  83. 83.
    Montesinos P, Gouet V, Deriche R (1998) Differential invariants for color images. In: Proceedings of the international conference on pattern recognition, Brisbane (Australie), vol 1, pp 838–840Google Scholar
  84. 84.
    Montesinos P, Gouet V, Deriche R, Pel D (2000) Matching color uncalibrated images using differential invariants. Image Vis Comput 18(9):659–671CrossRefGoogle Scholar
  85. 85.
    Moosmann F, Larlus D, Jurie F (2006) Learning Saliency Maps for Object Categorization. In: ECCV international workshop on the representation and use of prior knowledge in visionGoogle Scholar
  86. 86.
    Moravec H (1977) Towards automatic visual obstacle avoidance. In: Proceedings of the 5th international joint conference on artificial intelligence, p 584Google Scholar
  87. 87.
    Nilsback ME, Zisserman A (2008) Automated flower classification over a large number of classes. In: Proceedings of the indian conference on computer vision, graphics image processing, pp 722 –729Google Scholar
  88. 88.
  89. 89.
    Qiu G (2002) Indexing chromatic and achromatic patterns for content-based colour image retrieval. Pattern Recogn 35(8):1675–1686MATHCrossRefGoogle Scholar
  90. 90.
    Quelhas P, Odobez J (2006) Natural scene image modeling using color and texture visterms. In: Proceedings of conference on image and video retrieval, Phoenix, USA, pp 411–421Google Scholar
  91. 91.
    Recognition benchmark images. URL
  92. 92.
    Reisfeld D, Wolfson H, Yeshurun Y (1995) Context-free attentional operators: the generalized symmetry transform. Int J Comput Vis 14:119–130CrossRefGoogle Scholar
  93. 93.
    Rosenberg C, Hebert M, Thrun S (2001) Color constancy using kl-divergence. In: IEEE international conference on computer vision, pp 239–246Google Scholar
  94. 94.
    Rutishauser U, Walther D, Koch C, Perona P (2004) Is bottom-up attention useful for object recognition? In: IEEE conference on computer vision and pattern recognition (CVPR), pp 37–44Google Scholar
  95. 95.
    van de Sande K, Gevers T, Snoek C (2010a) Evaluating color descriptors for object and scene recognition. IEEE Trans Pattern Anal Mach Intell 32:1582–1596CrossRefGoogle Scholar
  96. 96.
    van de Sande KE, Gevers T, Snoek CG (2010) Evaluating color descriptors for object and scene recognition. IEEE Trans Pattern Anal Mach Intell 32:1582–1596CrossRefGoogle Scholar
  97. 97.
    Schugerl P, Sorschag R, Bailer W, Thallinger G (2007) Object re-detection using sift and mpeg-7 color descriptors. In: Proceedings of the international workshop on multimedia content analysis and mining, pp 305–314Google Scholar
  98. 98.
    Sebe N, Gevers T, Dijkstra S, van de Weije J (2006a) Evaluation of intensity and color corner detectors for affine invariant salient regions. In: Proceedings of the 2006 conference on computer vision and pattern recognition workshop, IEEE computer society, Washington, DC, USA, CVPRW ’06, pp 18–25Google Scholar
  99. 99.
    Sebe N, Gevers T, van de Weijer J, Dijkstra S (2006) Corners detectors for affine invariant salient regions: is color important? In: Proceedings of conference on image and video retrieval, Phoenix, USA, pp 61–71Google Scholar
  100. 100.
    Shafer SA (1985) Using color to separate reflection components. Color Res Appl 10(4):210–218CrossRefGoogle Scholar
  101. 101.
    Shi L, Funt B, Hamarneh G (2008) Quaternion color curvature. In: Proceeding IS&T sixteenth color imaging conference, Portland, pp 338–341Google Scholar
  102. 102.
    Sikora T (2001) The mpeg-7 visual standard for content description - an overview. IEEE Trans Circ Syst Video Technol 11:696–702CrossRefGoogle Scholar
  103. 103.
    Song X, Muselet D, Tremeau A (2009) Local color descriptor for object recognition across illumination changes. In: Proceedings of the conference on advanced concepts for intelligent vision systems (ACIVS’09), Bordeaux (France), pp 598–605Google Scholar
  104. 104.
    Stentiford FWM (2003) An attention based similarity measure with application to content-based information retrieval. In: Proceedings of the storage and retrieval for media databases conference, SPIE electronic imagingGoogle Scholar
  105. 105.
    Stoettinger J, Hanbury A, Sebe N, Gevers T (2007) Do colour interest points improve image retrieval? In: Proceedings of the IEEE international conference on image processing, San Antonio (USA), vol 1, pp 169–172Google Scholar
  106. 106.
    Stokes M, Anderson M, Chandrasekar S, Motta R (1996) A standard default color space for the internet-srgb, Available from
  107. 107.
    Tuytelaars T, Mikolajczyk K (2008) Local invariant feature detectors: a survey. Found Trends Comput Graph Vis 3(3):177–280CrossRefGoogle Scholar
  108. 108.
    Vazquez E, Gevers T, Lucassen M, van de Weijer J, Baldrich R (2010) Saliency of color image derivatives: a comparison between computational models and human perception. J Opt Soc Am A 27(3):613–621CrossRefGoogle Scholar
  109. 109.
    Vázquez-Martína R, Marfila R, nez PN, Bandera A, Sandoval F (2009) A novel approach for salient image regions detection and description. Pattern Recogn Lett 30:1464–1476Google Scholar
  110. 110.
    Vigo DAR, Khan FS, van de Weijer J, Gevers T (2010) The impact of color on bag-of-words based object recognition. In: International conference on pattern recognition, pp 1549–1553Google Scholar
  111. 111.
    Vogel J, Schiele B (2004) A semantic typicality measure for natural scene categorization. In: Rasmussen CE, Blthoff HH, Schlkopf B, Giese MA (eds) Pattern recognition, lecture notes in computer science, vol 3175, Springer Berlin/Heidelberg, pp 195–203Google Scholar
  112. 112.
    Walther D, Rutishauser U, Koch C, Perona P (2005) Selective visual attention enables learning and recognition of multiple objects in cluttered scenes. Comput Vis Image Understand 100:41–63CrossRefGoogle Scholar
  113. 113.
    Wandell B (1987) The synthesis and analysis of color images. IEEE Trans Pattern Anal Mach Intell 9:2–13CrossRefGoogle Scholar
  114. 114.
    van de Weijer J, Schmid C (2006) Coloring local feature extraction. In: Proceedings of the ninth European conference on computer vision, Graz, Austria, vol 3954, pp 334–348Google Scholar
  115. 115.
    van de Weijer J, Schmid C (2007) Applying color names to image description. In: Proceedings of the IEEE international conference on image processing, San Antonio (USA), vol 3, pp 493–496Google Scholar
  116. 116.
    van de Weijer J, Gevers T, Geusebroek JM (2005) Edge and corner detection by photometric quasi-invariants. IEEE Trans Pattern Anal Mach Intell 27(4):625–630CrossRefGoogle Scholar
  117. 117.
    van de Weijer J, Gevers T, Bagdanov A (2006) Boosting color saliency in image feature detection. IEEE Trans Pattern Anal Mach Intell 28(1):150–156CrossRefGoogle Scholar
  118. 118.
    van de Weijer J, Gevers T, Smeulders A (2006b) Robust photometric invariant features from the colour tensor. IEEE Trans Image Process 15(1):118–127CrossRefGoogle Scholar
  119. 119.
    Wu P, Kong L, Li X, Fu K (2008a) A hybrid algorithm combined color feature and keypoints for object detection. In: Proceedings of the 3rd IEEE conference on industrial electronics and applications, Singapore, pp 1408–1412Google Scholar
  120. 120.
    Wu P, Kong L, Zhao F, Li X (2008) Particle filter tracking based on color and sift features. In: Proceedings of the international conference on audio, language and image processing, ShanghaiGoogle Scholar
  121. 121.
    Wurtz R, Lourens T (2000) Corner detection in color images through a multiscale combination of end-stopped cortical cells. Image Vis Comput 18(6-7):531–541CrossRefGoogle Scholar
  122. 122.
    Wyszecki G, Stiles WS (1982) Color science: concepts and methods, quantitative data and formulas, 2nd ed. Wiley, New YorkGoogle Scholar
  123. 123.
    Xiong W, Funt B (2006) Color constancy for multiple-illuminant scenes using retinex and svr. In: Proceeding of imaging science and technology fourteenth color imaging conference, pp 304–308Google Scholar
  124. 124.
    Zhang D, Wang W, Gao W, Jiang S (2007) An effective local invariant descriptor combining luminance and color information. In: Proceedings of IEEE international conference on multimedia and expo, Beijing (China), pp 1507–1510Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Laboratory Hubert Curien, UMR CNRS 5516Jean Monnet UniversitySaint-EtienneFrance
  2. 2.School of Computing ScienceSimon Fraser UniversityBurnabyCanada

Personalised recommendations