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Automatic Image Annotation using SURF Features
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 68 - Number 4 |
| Year of Publication: 2013 |
| Authors: Tuhin Shukla, Nishchol Mishra, Sanjeev Sharma |
10.5120/11567-6868
|
Tuhin Shukla, Nishchol Mishra, Sanjeev Sharma . Automatic Image Annotation using SURF Features. International Journal of Computer Applications. 68, 4 ( April 2013), 17-24. DOI=10.5120/11567-6868
Abstract
Automatic image annotation is a challenging field with a far reaching effect. As the world moves towards becoming more and more dependent on digital technologies every day, use of machine to automatically annotate images can be proved as demanding in many fields of image processing. Automatic Image Annotation reduces the gap between low level image features and high level image semantics. Utilization of Speeded Up Robust Features (SURF) in automatic image annotation is very appealing due to the fact that SURF is scale and rotation invariant detector and descriptor and is much faster than any other schemes. Unlike other methods SURF features use the entire image instead of segmented blocks of image. That is why annotation of images by using SURF can be considered as more accurate. In this paper, a SVM based image annotation approach is proposed that uses SURF features of image for annotation purpose. The experiments suggest that the method proposed is much more efficient than other methods.
References
- Gonzalez R. , Woods R, Eddins S, Digital Image Processing Using MATLAB, 2nd ed. , Pearson Education.
- Olson, David L. , Delen, Dursun, Advanced Data Mining Techniques, 2008, XII, Springer Publications
- Han, Kamber, Pei, Data Mining: Concepts and Techniques, 3rd ed. , Morgan Kaufmann.
- Dengsheng Zhang, Md. Monirul Islam, Guojun Lu, "A review on automatic image annotation techniques", Pattern Recognition, Volume 45, Issue 1, Pages 346-362, January 2012.
- Lei, Yinjie, et al. "An HMM-SVM-based automatic image annotation approach. " Computer Vision–ACCV 2010, Pages. 115-126, 2011.
- Li, X. , Snoek C. , Worring M. , "Learning Social Tag Relevance By neighbor voting", IEEE TRANSCATION MM, 11(7):1310-1322, 2009.
- Qi Xiaojun and Han Yutao. , "Incorporating multiple SVMs for automatic image annotation", Pattern Recognition. 40, 2, Pages. 728-741, February 2007.
- Bay, Herbert, Tinne Tuytelaars, and Luc Van Gool. "Surf: Speeded up robust features. " Computer Vision–ECCV 2006, Pages. 404-417, 2006.
- Goh, K. -S. ; Chang, E. Y. ; Li, B. ; , "Using one-class and two-class SVMs for multiclass image annotation," Knowledge and Data Engineering, IEEE Transactions on , vol. 17, no. 10, pp. 1333- 1346, Oct. 2005.
- Shi R. , Feng H. , Chua T. S. , Lee C. H. , 'An adaptive image content representa- tion and segmentation approach to automatic image annotation", Proceedings of the International Conference on Image and Video Retrieval, pp. 545–554, 2004.
- Cusano C. , Ciocca G. ,Schettini R. , "Image annotation using SVM", Proceedings of the Internet Imaging IV, vol. 5304, SPIE, 2004.
- Jeon, Jiwoon, Victor Lavrenko, and Raghavan Manmatha. "Automatic image annotation and retrieval using cross-media relevance models. " Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval. ACM, 2003.
- Chapelle, O. ; Haffner, P. ; Vapnik, V. N. , "Support vector machines for histogram-based image classification ," Neural Networks, IEEE Transactions on , vol. 10, no. 5, pp. 1055-1064, Sep 1999.
- Chih-Chung Chang and Chih-Jen Lin, "LIBSVM: a library for support vector machines", ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011.
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