Comprehensive colour image normalization
The same scene viewed under two different illuminants induces two different colour images. If the two illuminants are the same colour but are placed at different positions then corresponding rgb pixels are related by simple scale factors. In contrast if the lighting geometry is held fixed but the colour of the light changes then it is the individual colour channels (e.g. all the red pixel values or all the green pixels) that are a scaling apart. It is well known that the image dependencies due to lighting geometry and illuminant colour can be respectively removed by normalizing the magnitude of the rgb pixel triplets (e.g. by calculating chromaticities) and by normalizing the lengths of each colour channel (by running the ‘grey-world’ colour constancy algorithm). However, neither normalization suffices to account for changes in both the lighting geometry and illuminant colour.
In this paper we present a new comprehensive image normalization which removes image dependency on lighting geometry and illumination colour. Our approach is disarmingly simple. We take the colour image and normalize the rgb pixels (to remove dependence on lighting geometry) and then normalize the r, g and b colour channels (to remove dependence on illuminant colour). We then repeat this process, normalize rgb pixels then r, g and b colour channels, and then repeat again. Indeed we repeat this process until we reach a stable state; that is reach a position where each normalization is idempotent. Crucially this iterative normalization procedure always converges to the same answer. Moreover, convergence is very rapid, typically taking just 4 or 5 iterations.
To illustrate the value of our “comprehensive normalization” procedure we considered the object recognition problem for three image databases that appear in the literature: Swain's database, the Simon Fraser database, Sang Wok Lee's database. In all cases, for recognition by colour distribution comparison, the comprehensive normalization improves recognition rates (the results are near perfect and in all cases improve on results reported in the literature). Also recognition for the composite database (comprising almost 100 objects) is also near perfect.
KeywordsQuery Image Colour Channel Colour Distribution Color Constancy Camera Response
Unable to display preview. Download preview PDF.
- [BL98]D. Berwick and S.W. Lee. A chromaticity space for specularity-, illumination color-and illumination pose-invariant 3-d object recognition. In ICCV98, page Session 2.3, 1998.Google Scholar
- [CB97]J.L. Crowley and F. Berard. Multi-modal tracking of faces for video communications. In CVPR 97, pages 640–645, 1997.Google Scholar
- [Cha95]S.S. Chatterjee. Color invariant object and texture recognition, 1995. MSc thesis, Simon Fraser University, School of Computing Science.Google Scholar
- [FCF96]G.D. Finlayson, S.S. Chatterjee, and B.V. Punt. Color angular indexing. In The Fourth European Conference on Computer Vision (Vol II), pages 16–27. European Vision Society, 1996.Google Scholar
- [FDB91]B.V. Funt, M.S. Drew, and M. Brockington. Recovering shading in color images. In The Second European Conference on Computer Vision, pages 124–132. Springer Verlag, 1992.Google Scholar
- [FDF94a]G.D. Finlayson, M.S. Drew, and B.V. Funt. Color constancy: Generalized diagonal transforms suffice. J. Opt. Soc. Am. A, 11:3011–3020, 1994.Google Scholar
- [FDF94b]G.D. Finlayson, M.S. Drew, and B.V. Funt. Spectral sharpening: Sensor transformations for improved color constancy. J. Opt. Soc. Am. A, 11(5):1553–1563, May 1994.Google Scholar
- [FF95]B.V. Funt and G.D. Finlayson. Color constant color indexing. IEEE transactions on Pattern analysis and Machine Intelligence, 1995.Google Scholar
- [GFF95]S.S. Chatterjee G.D. Finlayson and B.V. Funt. Color angle invariants for object recognition. In 3rd IS&T and SID Color Imaging Conference, pages 44–47. 1995.Google Scholar
- [GJT88]R. Gershon, A.D. Jepson, and J.K. Tsotsos. From [r, g, b] to surface reflectance: Computing color constant descriptors in images. In International Joint Conference on Artificial Intelligence, pages 755–758, 1987.Google Scholar
- [Hun95]R.W.G. Hunt. The Reproduction of Color. Fountain Press, 5th edition, 1995.Google Scholar
- [LL97]S. Lin and S.W. Lee. Using chromaticity distributions and eigenspace analysis for pose-, illumination-and specularity-invariant recognition of 3d object. In CVPR97, pages 426–431, 1997.Google Scholar
- [MMK95]J. Matas, R. Marik, and J. Kittler. On representation and matching of multi-coloured objects. In Proceedings of the fifth International Conference on Computer Vision, pages 726–732. IEEE Computer Society, June 1995.Google Scholar
- [NB93]W. Niblack and R. Barber. The QBIC project: Querying images by content using color, texture and shape. In Storage and Retrieval for Image and Video Databases I, volume 1908 of SPIE Proceedings Series. 1993.Google Scholar
- [Pet93]A.P. Petrov. On obtaining shape from color shading. COLOR research and application, 18(4):236–240, 1993.Google Scholar
- [SO95]M. A. Stricker and M. Orengo. Similarity of color images. In Storage and Retrieval for Image and Video Databases III, volume 2420 of SPIE Proceedings Series, pages 381–392. Feb. 1995.Google Scholar
- [SW95]B. Schiele and A. Waibel. Gaze tracking based on face-color. In International Workshop on Automatic Face-and Gesture-Recognition, June 1995.Google Scholar