Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30172
Salient Points Reduction for Content-Based Image Retrieval

Authors: Yao-Hong Tsai

Abstract:

Salient points are frequently used to represent local properties of the image in content-based image retrieval. In this paper, we present a reduction algorithm that extracts the local most salient points such that they not only give a satisfying representation of an image, but also make the image retrieval process efficiently. This algorithm recursively reduces the continuous point set by their corresponding saliency values under a top-down approach. The resulting salient points are evaluated with an image retrieval system using Hausdoff distance. In this experiment, it shows that our method is robust and the extracted salient points provide better retrieval performance comparing with other point detectors.

Keywords: Barnard detector, Content-based image retrieval, Points reduction, Salient point.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1061832

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1106

References:


[1] M. Fiala, "Using normalized interest point trajectories over scale for image search," Proceedings of the 3rd Canadian Conference on Computer and Robot Vision, CRV 2006. pp. 58 - 58.
[2] Z.H. Zhang Y. Quan, W.H. Li, W. Guo, "A New Content-Based Image Retrieval", Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, Dalian, 13-16 August 2006. pp. 385-388.
[3] R. Datta, D. Joshi, J. Li, and J. Z. Wang, "Image retrieval: ideas, influences, and trends of the new age," ACM Computing Surveys, vol. 40, no. 2, Article 5, 2008.
[4] H. J. Lin, Y. T. Kao, S. H. Yen, and C. J. Wang, "A study of shape-based image retrieval," Proceedings of the 24th International Conference on Distributed Computing Systems Workshops, ICDCSW 2004. pp. 118- 123.
[5] L. Birgale, M. Kokare, and D. Doye, "Colour and texture features for content based image retrieval," Proceedings of the International Conference on Computer Graphics, Imaging and Visualisation, CGIV 2006. pp. 146-149.
[6] J. W. Han and L. Guo, "New image retrieval approach based on interest points", Proceedings of SPIE, vol. 4862, 2002, pp. 197-197.
[7] E. Loupias, N. Sebe, S. Bres, and J.-M. Jolion, "Wavelet-based salient points for image retrieval," Proceedings of International Conference on Image Processing, vol. 2, 2000, pp. 518-521.
[8] H. Ling, and D. W. Jacobs, "Deformation invariant image matching," Proceedings of the Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 2, pp. 1466-1473.
[9] J. Wang, H. Zha, and R. Cipolla,"Combining interest points and edges for content-based image retrieval," Processings of the ICIP in IEEE International Conference, 2005. pp. III - 1256-9.
[10] Md. M. Rahman, B. C. Desai, and P. Bhattacharya, "Visual keyword-based image retrieval using latent semantic indexing, correlation-enhanced similarity matching and query expansion in inverted index," 10th International Database Engineering and Applications Symposium, IDEAS 2006, pp. 201-208.
[11] G. Ding, Q. Dai, W. Xu, and F. Yang, "Affine-invariant image retrieval based on Wavelet interest points," Processings of the IEEE Multimedia Signal, 2005, pp. 1-4.
[12] C. Schmid and R. Mohr, "Local gray value invariants for image retrieval," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 5, pp. 530-535, 1997.
[13] P. B. Albee and G. C. Stockman, "Interest points from the radial mass transform," IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2, pp. 261-264, 2005.
[14] H. Song, B. Li and L. Zhang, "Color salient points detection using wavelet," Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, China, June 21-23, 2006, pp. 10298-10301.
[15] S. T. Barnard, and W. B. Thompson, "Disparity analysis of images," IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 333-340, 1980.
[16] D. P. Huttenlocher, G. A. Klanderman, and W. J. Rucklidge, "Comparing images using the Hausdorff distance," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 9, pp. 850-863, 1993.
[17] B. Takacs, "Comparing face images using the modified hausdorff distance," Pattern Recognition, vol. 31, no. 12, pp. 1873-1881, 1998.
[18] Y. Wang, and C. Chua, "Robust face recognition from 2D and 3D images using structural Hausdorff distance", Proceedings of the ICARCV in International Conference, 2006. pp. 502 - 507.
[19] H. Tan, Y. Zhang, "A novel weighted Hausdorff distance for face localization," Image and Vision Computing, vol. 24, no. 7, 2006. pp. 656-662.