Abstract
The paper describes an innovative image annotation tool, based on a multi-class Support Vector Machine, for classifying image pixels in one of seven classes – sky, skin, vegetation, snow, water, ground, and man-made structures – or as unknown. These visual categories mirror high-level human perception, permitting the design of intuitive and effective color and contrast enhancement strategies. As a pre-processing step, a smart color balancing algorithm is applied, making the overall procedure suitable for uncalibrated images, such as images acquired by unknown systems under unknown lighting conditions.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. on PAMI 22 (2000)
Fan, J., Gao, Y., Luo, H., Xu, G.: Automatic image annotation by using concept-sensitive salient objects for image content representation. In: Proceedings of the 27th annual international conference on Research and development in information retrieval, Sheffield, United Kingdom, July 25-29 (2004)
Jeon, J., Lavrenko, V., Manmatha, R.: Retrieval using Cross-Media Relevance Models. In: Proceedings of the 26th Intl ACM SIGIR Conf., pp. 119–126 (2003)
Saarelma, H., Oittinen, P.: Automatic Picture Reproduction. Graphics Art in Finland 22(1), 3–11 (1993)
Kanamori, K., Kotera, H.: A Method for Selective Color Control in Perceptual Color Space. Journal of Imaging Technologies 35(5), 307–316 (1991)
MacDonald, L.: Framework for an image sharpness management system. In: IS&T/SID 7th Color Imaging Conference, Scottsdale, pp. 75–79 (1999)
Fredembach, C., Schröder, M., Süsstrunk, S.: Region-based image classification for automatic color correction. In: Proc. IS&T/SID 11th Color Imaging Conference, pp. 59–65 (2003)
Fredembach, C., Schröder, M., Süsstrunk, S.: Eigenregions for image classification. In: Accepted for publication in IEEE Transactions on Pattern Analysis and Machine Intelligence (2004)
Gasparini, F., Schettini, R.: Color Balancing of Digital Photos Using Simple Image Statistics. Pattern Recognition 37, 1201–1217 (2004)
Stricker, M., Swain, M.: The Capacity of Color Histogram Indexing. Computer Vision and Pattern Recognition, 704–708 (1994)
Pass, G., Zabih, R.: Comparing Images Using Joint Histograms. Multimedia Systems 7(3), 234–240 (1999)
Cox, I.J., Miller, M.L., Omohundro, S.M., Yianilos, P.N.: Target Testing and the PicHunter Bayesian Multimedia Retrieval System. Advances in Digital Libraries, 66–75 (1996)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1955)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, Heidelberg (2001)
Cortes, C., Vapnik, V.: Support-Vector Networks. Machine Learning 20(3), 273–297 (1995)
Osuna, E., Freund, R., Girosi, F.: Training support vector machines. An application to face detection. In: Proceedings of CVPR 1997 (1997)
Blanz, V., Schölkopf, B., Bülthoff, H., Burges, C., Vapnik, V., Vetter, T.: Comparison of view-based object recognition algorithms using realistic 3D models. In: Vorbrüggen, J.C., von Seelen, W., Sendhoff, B. (eds.) ICANN 1996. LNCS, vol. 1112, pp. 251–256. Springer, Heidelberg (1996)
Muller, K.R., Mika, S., Ratsch, G., Tsuda, K., Scholkopf, B.: An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks 12(2), 181–201 (2001)
Weston, J., Watkins, C.: Support vector machines for multiclass pattern recognition. In: Proc. Seventh European Symposium On Artificial Neural Networks (1999)
Goh, K., Chang, E., Cheng, K.: Support vector machine pairwise classifiers with error reduction for image classification. In: Proc. ACM workshops on Multimedia: multimedia information retrieval, pp. 32–37 (2001)
Cusano, C., Ciocca, G., Schettini, R.: Image annotation using SVM. In: Proc Internet imaging V. SPIE, vol. 5304, pp. 330–338 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cusano, C., Gasparini, F., Schettini, R. (2006). Image Annotation for Adaptive Enhancement of Uncalibrated Color Images. In: Bres, S., Laurini, R. (eds) Visual Information and Information Systems. VISUAL 2005. Lecture Notes in Computer Science, vol 3736. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11590064_19
Download citation
DOI: https://doi.org/10.1007/11590064_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30488-3
Online ISBN: 978-3-540-32339-6
eBook Packages: Computer ScienceComputer Science (R0)