Automated image-orientation detection: A scalable boosting approach

Pattern Analysis and Applications

Volumn 10, Issue 3, 2007, Pages 247-263

  Baluja, Shumeet ^a  

^a GOOGLE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 34547442345     PISSN: 14337541     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10044-006-0059-1     Document Type: Article

References (22)

1. Vailaya A, Zhang H, Yang C, Liu F, Jain A (2002) Automatic image orientation detection. IEEE Trans Image Process 11:7
2. Wang Y, Zhang H (2001) Content-based image orientation detection with support vector machines. In: IEEE workshop on content-based access of image and video libraries, pp 17-23
3. Wang Y, Zhang H (2004) Detecting image orientation based on low level visual content. Comput Vis Image Underst 93(3):328-346
4. Zhang, L, Li M, Zhang, H (2002) Boosting image orientation detection with indoor versus outdoor classification. In: Workshop on application of computer vision
5. Luo J, Boutell M (2005) A probabilistic approach to image orientation detection via confidence-based integration of low level and semantic cues. IEEE Trans Pattern Anal Mach Intell 27(5):715-726
6. Lyu S (2005) Automatic image orientation determination with natural image statistics. In: Proceedings of the thirteenth annual ACM international conference on multimedia, pp 491-494
7. Wang L, Liu X, Xia, L, Xu G, Bruckstein A (2003) Image orientation detection with integrated human perception cues (or which way is up). ICIP
8. Baluja S, Rowley HA (2005) Boosting sex identification performance. In: Proceedings of the twentieth national conference on artificial intelligence and the seventeenth innovative applications of artificial intelligence conference, pp 1508-1513
9. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient based learning applied to document recognition. Proc IEEE 86:11
10. Rowley H, Baluja S, Kanade T (1998) Neural network-based face detection. IEEE Trans Pattern Anal Mach Intell 20(1):23-28
11. Viola P, Jones MJ (2001) Rapid object detection using a boosted cascade of simple features. In: 2001 IEEE computer society conference on computer vision and pattern recognition (CVPR'01), p 511
12. Freund Y, Schapire R (1996) Experiments with a new boosting algorithm. In: Machine learning, proceedings of the thirteenth international conference
13. Wu J, Rehg J, Mullins M (2003) Learning a rare event detection cascade by direct feature selection. Neural Inf Process Syst 16
14. Cortes C, Vapnik V (1995) Support-vector-networks. Mach Learn 20(3):273-297
15. Joachims T (1999) Making large-scale SVM learning practical. In: Schölkopf B, Burges C, Smola A (eds) Advances in Kernel methods-support vector learning
16. Corel-Gallery 1,300,000 (1999) Image gallery-16 CD Set-JB #40629
17. Shapiro L (2002) University of Washington image database for content-based image retrieval. available from http://www.cs.washington.edu/ research/imagedatabase/report03.html
18. Dietterich TG (2000) Ensemble methods in machine learning. In: Roli I, Kittler J (eds) First international workshop on multiple classifier systems. Lecture notes in computer science. Springer, Berlin Heidelberg New York, pp 1-15
19. Ghosh J (2002) MultiClassifier systems: back to the future. In: Roli I, Kittler J (eds) Multiple classifier systems. LNCS 2364:1-15
20. Luo J, Crandall D, Singhal A, Boutell M, Gray R (2003) Psychophysical study of image orientation perception. Spat Vis 16(5):429-457
21. Adobe Inc., Adobe Photoshop Elements, 4.0, http://www.adobe.co.uk/ products/photoshopelwin/main.html
22. Google, Inc., Picasa Photo Management Software, 2.0. http://www.picasa. google.com/