Content Based Image Retrieval using Exact Legendre moments and support vector machine

Aus de_evolutionary_art_org
Wechseln zu: Navigation, Suche


Referenz

Srinivasa Rao, Srinivas Kumar and Chandra Mohan: Content Based Image Retrieval using Exact Legendre moments and support vector machine. The International Journal of Multimedia & its Applications (IJMA), Vol.2, No.2, pp. 69-79,May 2010

DOI

http://dx.doi.org/10.5121/ijma.2010.2206

Abstract

Content Based Image Retrieval (CBIR) systems based on shape using invariant image moments, viz., Moment Invariants (MI) and Zernike Moments (ZM) are available in the literature. MI and ZM are good at representing the shape features of an image. However, non-orthogonality of MI and poor reconstruction of ZM restrict their application in CBIR. Therefore, an efficient and orthogonal moment based CBIR system is needed. Legendre Moments (LM) are orthogonal, computationally faster, and can represent image shape features compactly. CBIR system using Exact Legendre Moments (ELM) for gray scale images is proposed in this work. Superiority of the proposed CBIR system is observed over other moment based methods, viz., MI and ZM in terms of retrieval efficiency and retrieval time. Further, the classification efficiency is improved by employing Support Vector Machine (SVM) classifier. Improved retrieval results are obtained over existing CBIR algorithm based on Stacked Euler Vector (SERVE) combined with Modified Moment Invariants (MMI).

Extended Abstract

Bibtex

@article{
author = {Srinivasa Rao, Srinivas Kumar and Chandra Mohan},
title = {Content Based Image Retrieval using Exact Legendre moments and support vector machine},
journal = {The International Journal of Multimedia & its Applications (IJMA)},
volume = {2},
number = {2},
pages = {69-79},
year = {2010},
doi={},
url={http://dx.doi.org/10.5121/ijma.2010.2206 http://de.evo-art.org/index.php?title=Content_Based_Image_Retrieval_using_Exact_Legendre_moments_and_support_vector_machine},
}

Used References

[1] Arnold. W. M. Smeulders, M. Worring, S. Satini, A. Gupta, R. Jain, (2000) “Content - Based Image Retrieval at the end of the early years”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 12, pp. 1349-1380.

[2] M.K.Hu, (1962) “Visual pattern recognition by moment invariants”, IRE Transactions of Information Theory, 8 (1), pp. 179-187.

[3] X. Fu, Y.Li, R. Harrison, and S. Belkasim, (2006) “Content based image retrieval using Gabor- Zernike features,” Proc. of the 18th IEEE International Conference on Pattern Recognition (ICPR’06), Vol. 2, pp. 417-420.

[4] M. Zhenjiang, (2000) “Zernike moment - based image shape analysis and its application”, Pattern Recognition Letters, Vol. 21, pp. 169-177.

[5] A.Khotanzad, Y.H.Hong, (1990) “Invariant image recognition by Zernike moments”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(5), pp. 489-498.

[6] J. D. Zhou, H. Z. Shu, L. M. Luo, W. X. Yu, (2002) “Two new algorithms for efficient computation of Legendre moments”, Pattern Recognition, Vol.35(5), pp. 1143-1152.

[7] P. T. Yap and R. Paramesaran, (2005), “An Efficient Method for the Computation of Legendre Moments,” IEEE Transactions on Pattern Recognition and Machine Intelligence, Vol.27, No.12, pp.1996-2002.

[8] S. X. Liao, M. Pawlak, (1996) “On image analysis by moments,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 18(3), pp. 254-266.

[9] Khalid M. Hosney, (2007) “Exact Legendre moments computation for gray level images”, Pattern Recognition, Vol. 40, pp. 3597-3605.

[10] S. A. Nane, S. K. Nayar, and H. Murase, (1996) “Columbia Object Image Library: COIL-20,” Dept. Comp. Sci., Columbia University, New York, Tech. Rep. CUCS-006-96.

[11] K. K. Seo, (2007) “An application of one-class support vector machines in content-based image retrieval,” Expert Systems with Applications, Vol. 33(2) , pp.491-498.

[12] A. Bishnu and B. B. Bhattacharya, (2007) “Stacked Euler Vector (SERVE): A Gray-Tone Image Feature Based on Bit-Plane Augmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 2, pp. 350-355.

[13] R. Datta, D. Joshi, J. Li, and J. Z. Wang, (2008) “Image Retrieval: Ideas, Influences, and Trends of the New Age”, ACM Computing Surveys, Vol. 40, No.2, Article 5.

[14] C. Campbell, (2000) “Algorithmic Approaches to Training Support Vector Machines: A Survey, ESANN, D-Facto Publications, pp. 27- 36.

[15] T. H. Reiss, (1991) “The Revised Fundamental Theorem of Moment Invariants”, IEEE Transactions on Pattern Recognition and Machine Intelligence, Vol. 13, No. 8, pp. 830-834.

[16] C. J. C. Burges, (1998) “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Discovery, Vol. 2, pp. 121-167.

[17] R. C. Gonzalez and R. E. Woods, (2006), “Digital Image Processing”, Pearson Education, Second Edition, 2003.

[18] W. Y. Kim and Y. S. Kim, (2000) “A region based shape descriptor using Zernike moments,” Signal Processing: Image Communication, Vol. 16, pp. 95-102.

[19] Y. S. Kim and W. Y. Kim, (1998) “Content-based trademark retrieval system using a visually salient features,” Image and Vision Computing, Vol. 16, pp. 931-939.

[20] M. Kokare, B. N. Chatterji and P. K. Biswas, (2002) “A Survey on Current Content Based Image Retrieval Methods,” IETE Journal of Research, Vol. 48, no. 3&4, pp. 261-271.

[21] M. Flickner etal., (1995) “Query by image and video content: the QBIC system,” IEEE Computer, Vol. 28(9), pp. 23-32.

[22] A.K.Jain and A.Vailaya, (1995) “Image Retrieval using Color and Shape,” Second Asian Conference on Computer Vision, Singapore, pp. 529-533.

[23] D. Zhang and G. Lu, (2004) “Review of shape representation and description techniques,” Pattern Recognition, Vol. 37, pp.1-19.

[24] J. Wang, H. Zha, R. Cipolla, (2005) “Combining interest points and edges for content- based image retrieval, IEEE International Conference on Image Processing.

Links

Full Text

http://www.airccse.org/journal/jma/0510ijma06.pdf

internal file


Sonstige Links