Abstract
Many imaging applications require that images are correctly orientated with respect to their content. In this work we present an algorithm for the automatic detection of the image orientation that relies on the image content as described by Local Binary Patterns (LBP). The detection is efficiently performed by exploiting logistic regression. The proposed algorithm has been extensively evaluated on more than 100,000 images taken from the Scene UNderstanding (SUN) database. The results show that our algorithm outperformed similar approaches in the state of the art, and its accuracy is comparable with that of human observers in detecting the correct orientation of a wide range of image contents.
Similar content being viewed by others
References
Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041
Appia VV, Narasimha R (2011) Low complexity orientation detection algorithm for real-time implementation. In: Proceedings of SPIE-IS & T electronic imaging on real-time image and video processing, vol 7871, p 787108
Baluja S (2007) Automated image-orientation detection: a scalable boosting approach. Pattern Anal Appl 10(3):247–263
Borawski M, Frejlichowski D (2012) An algorithm for the automatic estimation of image orientation. In: Perner P (ed) Machine learning and data mining in pattern recognition, of lecture notes in computer science, vol 7376. Springer, Berlin, pp 336–344
Ciocca G, Cusano C, Schettini R (2010) Image orientation detection using low-level features and faces. In: Society of photo-optical instrumentation engineers (SPIE) conference series, of society of photo-optical instrumentation engineers (SPIE) conference series, vol 7537, pp 75370R–75370R–8
Deng J, Berg AC, Li K, Fei-Fei L (2010) What does classifying more than 10,000 image categories tell us? In: Computer vision–ECCV 2010, pp 71–84
Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vis 88(2):303–338
Exchangeable image file format for digital still cameras (2002) EXIF version 2.2. JEITA CP-3451, Standard of Japan Electronics and Information Technology Industries Association
Fan R-E, Chang K-W, Hsieh C-J, Wang X-R, Lin C-J (2008) Liblinear: a library for large linear classification. J Mach Learn Res 9:1871–1874
Fellbaum C (1998) Wordnet: an electronic lexical database. Bradford Books
Huttunen S, Rahtu E, Kunttu I, Gren J, Heikkil J (2011) Real-time detection of landscape scenes. In: Heyden A, Kahl F (eds) Image analysis of lecture notes in computer science, vol 6688. Springer, Berlin, pp 338–347
Lin C-J, Weng RC, Sathiya Keerthi S (2008) Trust region Newton method for large-scale logistic large-scale logistic. J Mach Learn Res 9:627–650
Lumini A, Nanni L (2006) Detector of image orientation based on Borda count. Pattern Recogn Lett 27(3):180–186
Luo J, Boutell M (2005) Automatic image orientation detection via confidence-based integration of low-level and semantic cues. IEEE Trans Pattern Anal Mach Intell 27(5):715–726
Luo J, Crandall D, Singhal A, Boutell M, Gray RT (2003) Psychophysical study of image orientation perception. Spat Vis 16(5):429–457
Lyu S (2005) Automatic image orientation determination with natural image statistics. In: Proceedings of the 13th annual ACM international conference on multimedia, MULTIMEDIA ’05. ACM, pp 491–494
Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recognit 29(1):51–59
Pietikäinen M, Zhao G, Hadid A, Ahonen T (2011) Computer vision using local binary patterns. Number 40 in Computational Imaging and Vision, Springer
Takala V, Pietikainen M (2007) Multi-object tracking using color, texture and motion. In: IEEE conference on computer vision and pattern recognition, 2007. CVPR ’07, pp 1–7
Tolstaya E (2007) Content-based image orientation recognition. In: Proceedings of the international conference on computer graphics and vision, GraphiCon 2007, pp 158–161
Vailaya A, Zhang H, Yang C, Liu F-I, Jain AK (2002) Automatic image orientation detection. IEEE Trans Image Process 11(7):746–755
Wang L, Liu X, Xia L, Xu G, Bruckstein A (2003) Image orientation detection with integrated human perception cues (or which way is up). In: Proceedings of the 2003 international conference on image processing 2003, ICIP 2003, vol 2–3, pp II–539–542
Whang Y, Zhang H (2004) Detecting image orientation based on low-level visual content. Comp Vision Image Underst 93(3):328–346
Xiao J, Hays J, Ehinger KA, Oliva A, Torralba A (2010) Sun database: large-scale scene recognition from abbey to zoo. In: 2010 IEEE conference on computer vision and pattern recognition (CVPR), pp 3485–3492
Zhang L, Li M, Zhang H-J (2002) Boosting image orientation detection with indoor vs. outdoor classification. In: Proceedings of the sixth IEEE workshop on applications of computer vision, pp 95–99
Acknowledgments
We would like to thank Dr. Vikram Appia for the support to the implementation of his method.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ciocca, G., Cusano, C. & Schettini, R. Image orientation detection using LBP-based features and logistic regression. Multimed Tools Appl 74, 3013–3034 (2015). https://doi.org/10.1007/s11042-013-1766-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-013-1766-4