CONTENT BASED IMAGE RETRIEVAL WITH SURF, COLOR HISTOGRAM, SVM AND NN

Volume: 3    Issue: 3

Year of Publication: 2016

Pages: (1-6)

Authors: Apurva Sharma, Dr.Swati Sharma

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Abstract

Here present an efficient algorithm depend on SURF (Speeded up Robust Features), color histogram, SVM and NN. This method applies the SURF algorithm in the detect and description for images feature; first it applies the SURF feature detector in extracting reference images and matching feature points in the image, respectively. At last in the process of feature points matching; the false matching points are eliminated through this approach. Finally; according to the rest of the match point; it can estimate the space geometric transformation parameters between two images and thus matching process is completed. Here, use SURF algorithm to detect and descript the interest points; and match the interest points by using Surf [1,3]. In this thesis, the same is tried to retrieve with the use of SURF and fed into Support Vector Machine (SVM) and color histogram for further classification. SURF is fast and robust interest points detector which is used in many computer vision applications. To further enhance result use NN technique. To using these techniques to enhances the previous result.

References

1. B Zitova, J Flusser. Image registration methods: A survey. Image Vis. Comput. 2003; 21(11): 977-1000.

2. Dai X, Khorram S. Development of a Feature-Based Approach to Automated Image Registration for Multi temporal and Multi sensor Remotely Sensed Imagery. Proceedings of the IEEE Interactional Geoscience and Remoth Sensing Symposium. 1997; 1: 243-245.

3. Goshtasby A. Mbolieally-assisted approach to digital image registration with application in computer vision. Ph. D. dissertation, Dept. of Computer Science, Miehigan State University, TR83-013. 1983.

4. Ton J, Jain A. Stering Landsat images by point matehing. IEEE Transactions on Geoscience and Remote Sensing. 1989; 27(5): 642-651.

5. Li H, Manjunath B, Mitra S. Ntour-Based Approach to Multisensor Image Registration. IEEE Transactions on Image Processing. 1995; 4(3): 320-334.

6. Tham J, Ranganath S, Ranganath M, Kassim A. Vel Unrestricted Center-Biased Diamond Search Algorithm for Block Motion Estimation. IEEE Transactions on Circuits System and Video Technology. 1998; 8(4): 369-377.

7. Yuan Z, Wu F, Zhuang Y. I-sensor image registration using multi-resolution shape analysis. Journal Of Zhejiang University Science A. 2006; (4): 549-555.

8. Ziou D, Tabbone S. Dtection techniques-an overview. International Journal of Pattern Recognition and Image Analysis. 1988; (5): 537-559.

9. Mount D, Netanyahu N, Moigne J. Ent Algorithms for Robust Feature Matching. Pattern Recognition, 1999; 32(1): 17-38.

10. Bay H, Tuvtellars T, Gool L Van. SURF: speeded up robust features. Proceedings of the European Conference on Computer Vision. 2006: 404 417.

11. Rong W, Chen H, et al. Icing of Microscope Images based on SURF. 24th International Conference Image and Vision Computing New Zealand (IVCNZ2009). 2009: 272-275.

12. Hartley R, Zisserman A. View Geometry in Computer Vision, second. ed. Cambridge University Press, Cambridge. 2003.

13. Tola E, Lepetit V. St local descriptor for dense matching. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society. 2008: 1-8.

14. Tola E, Lepetit VY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo. IEEE Transactions on Pattern Analysis & Machine Intelligence. 2010; 32(5): 815-830.

15. Bracewell R. Fourier Transform and Its Applications. McGraw-Hill, New York. 1965.

16. Xiaoli Li, Xiaohong Wang and Chunsheng Li: Image Matching Based on Unification, The Fourth International Joint Conference on Computational Science and Optimization (2011), p. 825-828.

17. Herbert Bay, Andreas Ess, Tinne Tuytelaars and Luc Van Gool: Speeded-Up Robust Features (SURF), Computer Vision and Image Understanding (2008), 110(3), p. 346-359.

18. David G. Lowe: Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision (2004), 60(2), p. 91-110.

19. Luo Juan and Oubong Gwun: A Comparison of SIFT, PCA-SIFT and SURF, International Journal of Image Processing (2009), 3(4), p. 143-152.

20. Dorigo M., Maniezzo V. and Colorni A.: Ant System: Optimization by A Colony of Cooperating Agents, IEEE Transaction on Systems, Man, and Cybernetics-Part B (1996), 26(1), p. 29-41.

Keywords

Matching, Image processing, Surf, Color histogram, NN and SVM