This Article 
 Bibliographic References 
 Add to: 
Automatic Image Orientation Detection via Confidence-Based Integration of Low-Level and Semantic Cues
May 2005 (vol. 27 no. 5)
pp. 715-726
Jiebo Luo, IEEE
Matthew Boutell, IEEE Computer Society
Automatic image orientation detection for natural images is a useful, yet challenging research topic. Humans use scene context and semantic object recognition to identify the correct image orientation. However, it is difficult for a computer to perform the task in the same way because current object recognition algorithms are extremely limited in their scope and robustness. As a result, existing orientation detection methods were built upon low-level vision features such as spatial distributions of color and texture. Discrepant detection rates have been reported for these methods in the literature. We have developed a probabilistic approach to image orientation detection via confidence-based integration of low-level and semantic cues within a Bayesian framework. Our current accuracy is 90 percent for unconstrained consumer photos, impressive given the findings of a psychophysical study conducted recently. The proposed framework is an attempt to bridge the gap between computer and human vision systems and is applicable to other problems involving semantic scene content understanding.

[1] A. Vailaya, H.J. Zhang, and A. Jain, “Automatic Image Orientation Detection,” IEEE Trans. Image Processing, vol. 11, no. 7, pp. 746-755, 2002.
[2] Y. Wang and H. Zhang, “Content-Based Image Orientation Detection with Support Vector Machines,” Proc. IEEE Workshop Content-Based Access of Image and Video Libraries, 2001.
[3] R. Segur, “Using Photographic Space to Improve the Evaluation of Consumer Cameras,” Proc. IS&T Image Processing, Image Quality, Image Capture and Systems (PICS) Conf., 2000.
[4] J. Luo, D. Crandall, A. Singhal, M. Boutell, and R.T. Gray, “Psychophysical Study of Image Orientation Perception,” Spatial Vision, vol. 16, no. 5, pp. 429-457, 2003.
[5] B. Scholkopf, C. Burges, and A. Smola, Advances in Kernel Methods: Support Vector Learning. Cambridge, Mass.: MIT Press, 1999.
[6] R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification. New York: John Wiley & Sons, 2001.
[7] H. Schneiderman, “A Statistical Approach to 3D Object Detection Applied to Faces and Cars,” PhD thesis, CMU-RI-TR-00-06, Carnegie Mellon Univ., 2000.
[8] A. Vailaya and A. Jain, “Detecting Sky and Vegetation in Outdoor Images,” Proc. SPIE: Storage and Retrieval for Image and Video Databases VIII, vol. 3972, 2000.
[9] J. Luo and S.P. Etz, “A Physical Model-Based Approach to Sky Detection in Photographic Images,” IEEE Trans. Image Processing, vol. 11, no. 3, pp. 201-212, 2002.
[10] R.P.W. Duin, “The Combining Classifier: To Train or Not to Train?” Proc. Int'l Conf. Pattern Recognition, 2002.
[11] J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Francisco: Morgan Kaufmann Publishers, Inc., 1988.
[12] A. Singhal, J. Luo, and W. Zhu, “Probabilistic Spatial Context Models for Scene Content Understanding,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 2003.
[13] C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121-167, 1998.
[14] D.M.J. Tax and R.P.W. Duin, “Using Two-Class Classifiers for Multiclass Classification,” Proc. Int'l Conf. Pattern Recognition, 2002.
[15] G. Cooper and E. Herskovits, “A Bayesian Method for the Induction of Probabilistic Networks from Data,” Machine Learning, vol. 9, pp. 309-347, 1992.
[16] J.R. Smith and C.-S. Li, “Image Classification and Querying Using Composite Region Templates,” Computer Vision and Image Understanding, vol. 75, nos. 1-2, pp. 165-174, 1999.
[17] Z. Tu and S.-C. Zhu, “Image Segmentation by Data-Driven Markov Chain Monte Carlo,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 657-673, May 2002.
[18] D. Comaniciu and P. Meer, “Mean Shift: A Robust Approach toward Feature Space Analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603-619, May 2002.
[19] R. Goodwin, Whole Order Orientation Method and Apparatus, US Patent #5642443, 1997.
[20] J. Luo, A. Singhal, S.P. Etz, R.T. Gray, “A Computational Approach to Determination of Main Subject Regions in Photographic Images,” Image Vision Computing, vol. 22, no. 3, pp. 227-241, 2004.

Index Terms:
Image orientation, semantic cues, low-level cues, Bayesian networks, probabilistic inference, classification confidence.
Jiebo Luo, Matthew Boutell, "Automatic Image Orientation Detection via Confidence-Based Integration of Low-Level and Semantic Cues," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 5, pp. 715-726, May 2005, doi:10.1109/TPAMI.2005.96
Usage of this product signifies your acceptance of the Terms of Use.