Image orientation detection using LBP-based features and logistic regression

详细信息    查看全文

• 作者：Gianluigi Ciocca (1)
  Claudio Cusano (2)
  Raimondo Schettini (1)
  
  1. Department of Informatics ; Systems and Communication (DISCo) ; Universit脿 degli Studi di Milano-Bicocca ; Viale Sarca 336 ; 20126 ; Milano ; Italy
  2. Department of Electrical ; Computer and Biomedical Engineering ; Universit脿 degli Studi di Pavia ; via Ferrata 1 ; 27100 ; Pavia ; Italy
  
• 关键词：Image orientation detection ; Low ; level features ; Local binary patterns ; Logistic regression ; Image classification
• 刊名：Multimedia Tools and Applications
• 出版年：2015
• 出版时间：May 2015
• 年：2015
• 卷：74
• 期：9
• 页码：3013-3034
• 全文大小：1,093 KB
• 参考文献：1. Ahonen, T, Hadid, A, Pietikainen, M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28: pp. 2037-2041 i.org/10.1109/TPAMI.2006.244" target="_blank" title="It opens in new window">CrossRef
  2. 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
  3. Baluja, S (2007) Automated image-orientation detection: a scalable boosting approach. Pattern Anal Appl 10: pp. 247-263 i.org/10.1007/s10044-006-0059-1" target="_blank" title="It opens in new window">CrossRef
  4. Borawski, M, Frejlichowski, D An algorithm for the automatic estimation of image orientation. In: itors">Perner, P eds. (2012) Machine learning and data mining in pattern recognition, of lecture notes in computer science, vol 7376. Springer, Berlin, pp. 336-344
  5. 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鈥?5370R鈥?
  6. Deng J, Berg AC, Li K, Fei-Fei L (2010) What does classifying more than 10,000 image categories tell us? In: Computer vision鈥揈CCV 2010, pp 71鈥?4
  7. Everingham, M, Gool, L, Williams, CKI, Winn, J, Zisserman, A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vis 88: pp. 303-338 i.org/10.1007/s11263-009-0275-4" target="_blank" title="It opens in new window">CrossRef
  8. 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
  9. 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: pp. 1871-1874
  10. Fellbaum C (1998) Wordnet: an electronic lexical database. Bradford Books
  11. Huttunen, S, Rahtu, E, Kunttu, I, Gren, J, Heikkil, J Real-time detection of landscape scenes. In: itors">Heyden, A, Kahl, F eds. (2011) Image analysis of lecture notes in computer science, vol 6688. Springer, Berlin, pp. 338-347
  12. Lin, C-J, Weng, RC (2008) Trust region Newton method for large-scale logistic large-scale logistic. J Mach Learn Res 9: pp. 627-650
  13. Lumini, A, Nanni, L (2006) Detector of image orientation based on Borda count. Pattern Recogn Lett 27: pp. 180-186 i.org/10.1016/j.patrec.2005.08.023" target="_blank" title="It opens in new window">CrossRef
  14. 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: pp. 715-726 i.org/10.1109/TPAMI.2005.96" target="_blank" title="It opens in new window">CrossRef
  15. Luo, J, Crandall, D, Singhal, A, Boutell, M, Gray, RT (2003) Psychophysical study of image orientation perception. Spat Vis 16: pp. 429-457 i.org/10.1163/156856803322552757" target="_blank" title="It opens in new window">CrossRef
  16. Lyu S (2005) Automatic image orientation determination with natural image statistics. In: Proceedings of the 13th annual ACM international conference on multimedia, MULTIMEDIA 鈥?5. ACM, pp 491鈥?94
  17. Ojala, T, Pietik盲inen, M, Harwood, D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recognit 29: pp. 51-59 i.org/10.1016/0031-3203(95)00067-4" target="_blank" title="It opens in new window">CrossRef
  18. Pietik盲inen M, Zhao G, Hadid A, Ahonen T (2011) Computer vision using local binary patterns. Number 40 in Computational Imaging and Vision, Springer
  19. Takala V, Pietikainen M (2007) Multi-object tracking using color, texture and motion. In: IEEE conference on computer vision and pattern recognition, 2007. CVPR 鈥?7, pp 1鈥?
  20. Tolstaya E (2007) Content-based image orientation recognition. In: Proceedings of the international conference on computer graphics and vision, GraphiCon 2007, pp 158鈥?61
  21. Vailaya, A, Zhang, H, Yang, C, Liu, F-I, Jain, AK (2002) Automatic image orientation detection. IEEE Trans Image Process 11: pp. 746-755 i.org/10.1109/TIP.2002.801590" target="_blank" title="It opens in new window">CrossRef
  22. 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鈥?, pp II鈥?39鈥?42
  23. Whang, Y, Zhang, H (2004) Detecting image orientation based on low-level visual content. Comp Vision Image Underst 93: pp. 328-346 i.org/10.1016/j.cviu.2003.10.006" target="_blank" title="It opens in new window">CrossRef
  24. 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鈥?492
  25. 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鈥?9
  
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems
  Computer Communication Networks
  Data Structures, Cryptology and Information Theory
  Special Purpose and Application-Based Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-7721

文摘

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.