Share This Article:

Categorization and Reorientation of Images Based on Low Level Features

Abstract Full-Text HTML Download Download as PDF (Size:658KB) PP. 1-10
DOI: 10.4236/jilsa.2011.31001    4,612 Downloads   9,192 Views   Citations

ABSTRACT

A hierarchical system to perform automatic categorization and reorientation of images using content analysis is pre-sented. The proposed system first categorizes images to some a priori defined categories using rotation invariant features. At the second stage, it detects their correct orientation out of {0o, 90o, 180o, and 270o} using category specific model. The system has been specially designed for embedded devices applications using only low level color and edge features. Machine learning algorithms optimized to suit the embedded implementation like support vector machines (SVMs) and scalable boosting have been used to develop classifiers for categorization and orientation detection. Results are presented on a collection of about 7000 consumer images collected from open resources. The proposed system finds it applications to various digital media products and brings pattern recognition solutions to the consumer electronics domain.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

R. Bhatt, G. Sharma, A. Dhall, N. Kumar and S. Chaudhury, "Categorization and Reorientation of Images Based on Low Level Features," Journal of Intelligent Learning Systems and Applications, Vol. 3 No. 1, 2011, pp. 1-10. doi: 10.4236/jilsa.2011.31001.

References

[1] D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International Journal on Computer Vision, Vol. 60, No. 2, 2004, pp. 91-110. doi:10.1023/B:VISI. 0000029664.99615.94
[2] J. Willamowski, D. Arregui, G. Csurka, C. R. Dance and L. Fan, “Categorizing Nine Visual Classes Using Local Appearance Descriptors,” International Conference on Pattern Recognition, Cambridge, 2004.
[3] L. Fei-Fei and P. Perona, “A Bayesian Hierarchical Model for Learning Natural Scene Categories,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, 20-25 June 2005, pp. 524-531.
[4] S. Lazebnik, C. Schmid and J. Ponce, “Beyond Bags of Features: Spatial Py-ramid Matching for Recognizing Natural Scene Categories,” Proceedings of Computer Vision and Pattern Recognition, IEEE, New York, 2006, pp. 2169-2178.
[5] A. Vailaya, H.-J. Zhang, C. Yang, F.-I. Liu and A. K. Jain, “Automatic Image Orientation Detection,” IEEE Transactions on Image Processing, Vol. 11, No. 7, 2002, pp. 746-755. doi:10.1109/TIP.2002.801590
[6] Y. Wang and H. Zhang, “Content-Based Image Orientation Detection with Support Vector Machines,” Proceedings of IEEE Workshop on Con-tent-Based Access of Image and Video Libraries, IEEE, New York, 2001, pp. 17-23. doi:10.1109/IVL.2001.990851
[7] Y. M. Wang and H.-J. Zhang, “Detecting Image Orientation Based on Low-Level Visual Content,” Computer Vision and Image Understanding, Vol. 93, No. 3, 2004, pp. 328-346. doi:10.1016/j.cviu.2003.10.006
[8] E. Tolstaya, “Con-tent-Based Image Orientation Recognition,” GraphiCon, Mos-cow, June 23-27 2007.
[9] S. Lyu, “Automatic Image Orien-tation Detection with Natural Image Statistics,” Proceedings of 13th International Multimedia Conference, Singapore, 6-11 November 2005, pp. 491-494. doi:10.1145/1101149.1101259
[10] S. Baluja, “Automated Image-Orientation Detection: A Scalable Boosting Approach,” Pattern Analysis and Applications, Vol. 10, No. 3, 2007, pp. 247-263. doi:10. 1007/s10044-006-0059-1
[11] S. Baluja and H. A. Rowley, “Large Scale Performance Measurement of Content-Based Automated Image- Orientation Detection,” Proceedings of International Conference on Image Processing, Vol. 2, 2005, pp. 514-517.
[12] H. Le Borgne and N. E. Oconnor, “Pre-Classification for Automatic Image Orientation,” Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Tou Louse, 14-19 May 2006, pp. 125-128 doi:10.1109/ICASSP.2006. 1660295
[13] J. Huang, S. R. Kumar, M. Mitra, W.-J. Zhu and R. Zabih, “Image Indexing Using Color Correlograms,” IEEE Computer Vision and Pattern Recognition, San Juan, 1997, pp. 762-768.
[14] B. Scholkopf and A. J. Smola, “Learning with Kernels : Support Vector Machines, Regularization, Optimization, and Beyond,” The MIT Press, Cambridge, 2001.
[15] C.-C. Chang and C.-J. Lin, “LIBSVM: A Library for Support Vector Machines,” 2001, http://www.csie,ntu.edu. tw/~cjlin/libsvm
[16] F. Crow, “Summed Area Tables for Texture Mapping,” Proceedings of Special Interest Group in Graphics, ACM, Minneapolis, 1984, pp. 207-212.
[17] Y. Freund and R. E. Schapire, “A Short Introduction to Boosting,” Journal of Japanese Society of Artificial Intelligence, Vol. 14, No. 5, 1999, pp. 771-780.
[18] R. Kohavi, “A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection,” Proceedings of 15th International Joint Conference on Artificial Intelligence, Nagoya, 23-29 August 1997, pp. 1137-1143.
[19] A. Oliva and A. Toralba, “Modeling the Shape of the Scene : A Holistic Representation of the Spatial Envelope,” International Journal on Computer Vision, Vol. 42, No. 3, 2001, pp. 145-175. doi:10.1023/A:1011139631724

  
comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.