Abstract
Accurately and automatically detecting image orientation is a task of great importance in intelligent image processing. In this paper, we present automatic image orientation detection algorithms based on these features: color moments; harris corner; phase symmetry; edge direction histogram. The statistical learning support vector machines, AdaBoost, Subspace classifier are used in our approach as classifiers. We use Borda Count as combination rule for these classifiers. Large amounts of experiments have been conducted, on a database of more than 6,000 images of real photos, to validate our approaches. Discussions and future directions for this work are also addressed at the end of the paper.
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References
Evano, M.G., McNeill, K.M.: Computer recognition of chest image orientation. In: Proc. Eleventh IEEE Symp. on Computer-Based Medical Systems, pp. 275–279 (1998)
Poz, A.P.D., Tommaselli, A.M.G.: Automatic absolute orientation of scanned aerial photographs. In: Proc. Internat. Symposium on Computer Graphics, Image Processing, and Vision, pp. 295–302 (1998)
Vailaya, A., Zhang, H., Jain, A.K.: Automatic image orientation detection. In: Proc. Sixth IEEE Internat. Conf. on Image Processing, pp. 600–604 (1999)
Vailaya, A., Jain, A.K.: Rejection option for VQ-based Bayesian classification. In: Proc. Fifteenth Internat. Conf. on Pattern Recognition, pp. 48–51 (2000)
Wang, Y.M., Zhang, H.: Detecting image orientation based on low-level visual content. Computer Vision and Image Understanding, 328–346 (2004)
Oja, E.: Subspace Methods of Pattern Recognition. Research Studies Press Ltd., Letchworth (1983)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, Chichester (2000)
Cristianini, N., Shawe-Taylor, J.: An introduction to Support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge (2000)
Viola, P., Jones, M.: Fast and robust classification using asymmtric AdaBoost and a detector cascade. In: NIPS 14 (2002)
Kittler, J., Roli, F. (eds.): 1st Int. Workshop on Multiple Classifier Systems. Springer, Cagliari, Italy (2000)
Breiman, L.: Bagging predictors. Mach. Learning (2), 123–140 (1996)
Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell., 832–844 (1998)
Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler and Roli, 2000, pp. 1–15 (2000)
Jain, A.K., Vailaya, A.: Image retrieval using color and shape. Pattern Recognition 29, 1233–1244 (1996)
Canny, J.F.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Fourth Alvey Vision Conference, pp. 147–151 (1988)
Peter, K.: Image Features From Phase Congruency. Videre: A Journal of Computer Vision Research, vol. 1(3). MIT Press, Cambridge (Summer 1999)
Zhang, L., Li, M., Zhang, H.: Boosting Image Orientation Detection with Indoor vs. Outdoor Classification. In: Sixth IEEE Workshop on Applications of Computer Vision (2002)
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Nanni, L., Lumini, A. (2005). Detector of Image Orientation Based on Borda-Count. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492542_29
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DOI: https://doi.org/10.1007/11492542_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26154-4
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