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Image Retrieval using Harris Corners and Histogram of Oriented Gradients

by K.Velmurugan, Lt. Dr. S.Santhosh Baboo
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 24 - Number 7
Year of Publication: 2011
Authors: K.Velmurugan, Lt. Dr. S.Santhosh Baboo
10.5120/2968-3968

K.Velmurugan, Lt. Dr. S.Santhosh Baboo . Image Retrieval using Harris Corners and Histogram of Oriented Gradients. International Journal of Computer Applications. 24, 7 ( June 2011), 6-10. DOI=10.5120/2968-3968

@article{ 10.5120/2968-3968,
author = { K.Velmurugan, Lt. Dr. S.Santhosh Baboo },
title = { Image Retrieval using Harris Corners and Histogram of Oriented Gradients },
journal = { International Journal of Computer Applications },
issue_date = { June 2011 },
volume = { 24 },
number = { 7 },
month = { June },
year = { 2011 },
issn = { 0975-8887 },
pages = { 6-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume24/number7/2968-3968/ },
doi = { 10.5120/2968-3968 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:10:20.397888+05:30
%A K.Velmurugan
%A Lt. Dr. S.Santhosh Baboo
%T Image Retrieval using Harris Corners and Histogram of Oriented Gradients
%J International Journal of Computer Applications
%@ 0975-8887
%V 24
%N 7
%P 6-10
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Content based image retrieval is the technique to retrieve similar images from a database that are visually similar to a given query image. It is an active and emerging research field in computer vision. In our proposed system, the Interest points based Histogram of Oriented Gradients (HOG) feature descriptor is used to retrieve the relevant images from the database. The dimensionality of the HOG feature vector is reduced by Principle Component analysis (PCA). To improve the retrieval accuracy of the system the Colour Moments along with HOG feature descriptor are used in this system. The Interest points are detected using the Harris-corner detector in order to extract the image features. The KD-tree is used for matching and indexing the features of the query image with the database images.

References
  1. R. C. Veltkamp and M. Tanase, “Content-based image retrieval systems:A survey,” Department of Computing Science, Utrecht University, Tech.Rep. UU-CS-2000-34, 2000.
  2. A. Halawani, A. Teynor, L. Setia, G. Brunner, and H. Burkhardt, "Fundamentals and Applications of Image Retrieval: An Overview", presented at Datenbank-Spektrum, 2006, pp.14-23.
  3. C. Schmid and R. Mohr. Local grayvalue invariants for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(5):530.534, 1997.
  4. T.Tuytelaars and L.Van Gool, “Content-based Image Retrieval based on Local Affinely Invariant Regions”.3rd Int. Conf. on Visual Information Systems, Visual99, Amsterdam, The Netharlands, 2-4 June 1999,pp.493-500.
  5. Harris and M.J. Stephens. ”A combined corner and edge detector”. In Proc. Alvey Vision Conference, pp. 147-152, 1988.
  6. K. Mikolajczyk. Interest point detection invariant to affine transformations. PhD thesis. Institut National Polytechnique de Grenoble. 2002.
  7. K. Mikolajczyk and C. Schmid. “An affine invariant interest point detector” in Proc. of the 7th European Conf. on Computer Vision. Copenhagen, Denmark, vol. I, pp. 128–142. 2002.
  8. D. Lowe. “Object Recognition from Local Scale-Invariant Features”, in Proc. of the 7th Int. Conf. on Computer Vision. vol. 2, p. 1150. September 20–25, 1999. IEEE, 1999.
  9. K. Mikolajczyk and C. Schmid (2001), Indexing based on scale invariant interest points., International Conference on Computer Vision, pp. 525-531.
  10. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR (1), pages 886–893, 2005.
  11. M. Stricker, and M. Orengo, “Similarity of color images”, SPIE Storage and Retrieval for Image and Video Databases III, vol. 2185, pp.381-392, Feb. 1995.
  12. Y. Ke and R. Sukthankar (2004), .PCA-SIFT: A more distinctive representation for a local image descriptors., Techn. Rep., INTEL Research.
  13. Jacob E. Goodman, Joseph O'Rourke and Piotr Indyk (Ed.) (2004). "Chapter 39 : Nearest neighbours in high-dimensional spaces". Handbook of Discrete and Computational Geometry (2nd ed.). CRC Press.
  14. S.A. Nene, S.K. Nayar and H. Murase. ”Columbia Object Image Library (COIL-100)”, TR CUCS-006-96, Dept. Comp. Sc., Columbia University, 1996.
  15. D. Lowe (2004), .Distinctive image features from scale-invariant keypoints., to appear in the International Journal of Computer Vision.
  16. K. Mikolajczk and C. Schmid (2003), .A performance evaluation of local descriptors., IEEE Conference on Computer Vision and Pattern Recognition.
  17. J.R. Smith and S. Chang. ”Single color extraction and image query”, in Proc. of IEEE Int’l Conf. on Image Processing, pp. 528-531, 1995.
  18. L. Huston Y. Ke, R. Sukthankar, “Efficient near-duplicate detection and sub-image retrieval,” in Proc. of ACM Multimedia (MM’04), 2004, 2004.
  19. R. Datta, D. Joshi, J. Li, and J. Z. Wang, “Image retrieval: Ideas, influences, and trends of the new age,” ACM Comput. Surv., vol. 40, no. 2, pp. 1–60, 2008.
  20. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain,“Content-based image retrieval at the end of the early years,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp.1349–1380, 2000.
  21. C. Wolf, W. Kropatsch, H. Bischof, and J.-M. Jolion. Content based image retrieval using interest points and texture features. International Conference on Pattern Recognition, 04:4234, 2000.
  22. H. Zhang, R. Rahmani, S. R. Cholleti, and S. A. Goldman. Local image representations using pruned salient points with applications to CBIR. In Proceedings of the 14th Annual ACM International Conference on Multimedia, 2006.
  23. T. Pavlidis, Limitations of CBIR, In ICPR ,2008.
  24. Xu Wangming Wu Jin Liu Xinhai Zhu Lei Shi Gang, “Application of Image SIFT Features to the Context of CBIR”, Int. Conference on computer Science and software engineering,Wuhan,Hubei Volume:4 page(s):552-555, ISBN: 978-0-7695-33360.
  25. Nguyen Duc Anh,Pham The Bao,Bui Ngoc Nam,Nguyen Huy Hoang, "A new CBIR system using Sift combined with neural network and graph-based segmentation", ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I Springer-Verlag Berlin, Heidelberg ©2010,ISBN:3-642-12144-6 978-3-642-12144-9.
  26. Zhang, R., and Zhang, Z, “FAST: toward more effective and efficient image retrieval”, ACM Multimedia Systems, 10(6), 529-543,2005..
  27. Horster, E., Lienhart, R., and Slaney, M. 2007. “Image retrieval on large-scale image databases”, In Proceedings of the 6th ACM International Conference on Image and Video Retrieval (Amsterdam, Netherlands), 17-24.
Index Terms

Computer Science
Information Sciences

Keywords

Content-based image retrieval interest points HOG KD-tree

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