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Reseach Article

Performance Comparison of Image Classifier using Discrete Cosine Transform and Walsh Transform

Published on None 2011 by H. B. Kekre, Tanuja Sarode, Meena S. Ugale
International Conference and Workshop on Emerging Trends in Technology
Foundation of Computer Science USA
ICWET - Number 4
None 2011
Authors: H. B. Kekre, Tanuja Sarode, Meena S. Ugale
dbb17dcb-7bf1-4b10-854e-4f31ee8d8ffc

H. B. Kekre, Tanuja Sarode, Meena S. Ugale . Performance Comparison of Image Classifier using Discrete Cosine Transform and Walsh Transform. International Conference and Workshop on Emerging Trends in Technology. ICWET, 4 (None 2011), 14-20.

@article{
author = { H. B. Kekre, Tanuja Sarode, Meena S. Ugale },
title = { Performance Comparison of Image Classifier using Discrete Cosine Transform and Walsh Transform },
journal = { International Conference and Workshop on Emerging Trends in Technology },
issue_date = { None 2011 },
volume = { ICWET },
number = { 4 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 14-20 },
numpages = 7,
url = { /proceedings/icwet/number4/2086-algo122/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference and Workshop on Emerging Trends in Technology
%A H. B. Kekre
%A Tanuja Sarode
%A Meena S. Ugale
%T Performance Comparison of Image Classifier using Discrete Cosine Transform and Walsh Transform
%J International Conference and Workshop on Emerging Trends in Technology
%@ 0975-8887
%V ICWET
%N 4
%P 14-20
%D 2011
%I International Journal of Computer Applications
Abstract

In recent years, the accelerated growth of digital media collections and in particular still image collections, both proprietary and on the Web, has established the need for the development of human-centered tools for the efficient access and retrieval of visual information. The need to manage these images and locate target images in response to user queries has become a significant problem. Image categorization is an important step for efficiently handling large image databases and enables the implementation of efficient retrieval algorithms.

References
  1. A.K.Jain and A.Vailaya, “Image retrieval using color and shape”, Pattern recognition, vol.29, no.8, pp.1233-1244, 1996.
  2. B.S.Manjunath and W.Y.Ma, “Texture feature for browsing and retrieval of image data”, IEEE PAMI, no. 18, vol. 8, pp. 837- 842, 1996.
  3. D. J. Le Gall, “The MPEG Video Compression Algorithm: A review,” SPIE 1452 (1991) 444-457.
  4. Dr. H.B.Kekre, Sudeep D. Thepade, Akshay Maloo, “Image Retrieval using Fractional Coefficients of Transformed Image using DCT and Walsh Transform”, International Journal of Engineering Science and Technology , Vol. 2(4), pp.362-371, 2010.
  5. Dr. H.B.Kekre, Tanuja K. Sarode, Sudeep D. Thepade, “Image Retrieval using Color-Texture Features from DCT on VQ Code vectors obtained by Kekre’s Fast Codebook Generation”, ICGST-GVIP Journal, Volume 9, Issue 5, September 2009.
  6. E. Nowak, F. Jurie, and B. Triggs. Sampling strategies for bag-of-features image classification. In ECCV,Part IV, LNCS 3954, pp. 490–503, 2006.
  7. Emma Newham, “The biometric report,” SJB Services, 1995.
  8. F. Li and P. Perona, “A Bayesian Hierarchical Model for Learning Natural Scene Categories,” Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, pp. 524-531, 2005.
  9. F.Mokhtarian and S.Abbasi, “Shape similarity retrieval under affine transforms”, Pattern Recognition, 2002, vol. 35, pp. 31-41.
  10. G. Csurka, C. Dance, L. Fan, J. Willamowski, and C. Bray. “Visual categorization with bags of key points”, In. Proc. ECCV'04 workshop on Statistical Learning in Computer. Vision , pp. 59–74,2004
  11. G. Griffin, A. Holub, and P. Perona. Caltech 256 objects category dataset. Technical Report UCB/CSD-04-1366, California Institute of Technology, 2007.
  12. G. K. Wallace, “Overview of the JPEG still Image Compression standard,” SPIE 1244 (1990) 220-233.
  13. Golam sorwar, Ajith abraham, “DCT based texture classification using soft computing approach”, Malaysian Journal of Computer Science, vol. 17,2004
  14. H. B. Kekre, Dhirendra Mishra, “Digital Image Search & Retrieval using FFT Sectors” published in proceedings of National/Asia pacific conference on Information communication and technology(NCICT 10) 5TH & 6TH March 2010..
  15. H. B. Kekre, Sudeep Thepade, Akshay Maloo, ”Performance Comparison of Image Retrieval Using Fractional Coefficients of Transformed Image Using DCT, Walsh, Haar and Kekre’s Transform”, CSC-International Journal of Image processing (IJIP), Vol.. 4, No.2, pp.:142-155, May 2010.
  16. H. B. Kekre, Tanuja Sarode, Shachi Natu, Prachi Natu, “Performance Comparison Of 2-D DCT On Full/Block Spectrogram And 1-D DCT On Row Mean Of Spectrogram For Speaker Identification”, CSC International Journal of Biometrics and Bioinformatics (IJBB), Volume (4): Issue (3).
  17. H.B.Kekre, Dhirendra Mishra, “Content Based Image Retrieval using Weighted Hamming Distance Image hash Value” published in the proceedings of international conference on contours of computing technology pp. 305-309 (Thinkquest2010) 13th & 14th March 2010.
  18. H.B.Kekre, Sudeep D. Thepade, “Improving the Performance of Image Retrieval using Partial Coefficients of Transformed Image”,International Journal of Information Retrieval, Serials Publications, Volume 2, Issue 1, 2009, pp. 72-79 (ISSN: 0974-6285).
  19. H.B.Kekre, Sudeep D. Thepade, Archana Athawale, Anant Shah, Prathmesh Verlekar, Suraj Shirke,“Energy Compaction and Image Splitting for Image Retrieval using Kekre Transform over Row and Column Feature Vectors”, International Journal of Computer Science and Network Security (IJCSNS),Volume:10, Number 1, January 2010, (ISSN: 1738-7906) Available at www.IJCSNS.org.
  20. H.B.Kekre, Sudeep D. Thepade, Archana Athawale, Anant Shah, Prathmesh Verlekar, Suraj Shirke, “Performance Evaluation of Image Retrieval using Energy Compaction and Image Tiling over DCT Row Mean and DCT Column Mean”, Springer-International Conference on Contours of Computing Technology (Thinkquest-2010), Babasaheb Gawde Institute of Technology, Mumbai, 13-14 March 2010, The paper will be uploaded on online Springerlink.
  21. J. Li and J. Wang, “Automatic Linguistic Indexing of Pictures by a statistical modeling approach”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, pp. 1075-1088, 2003.
  22. J.R.Smith and C.S.Li, “Image classification and quering using composite region templates”, Academic Press, Computer Vision and Understanding, 1999, vol.75, pp.165-174.
  23. J.Z.Wang, J.Li and G.Wiederhold, “SIMPLIcity: semantic sensitive integrated matching for picture libraries”, IEEE Trans. on Pattern Analysis and Machine Intelligence, 2001, vol.23, no.9, pp.947-963.
  24. M. Szummer and R.W. Picard, “Indoor-Outdoor Image Classification,” IEEE International Workshop on Content-based Access of Image and Video Databases, in conjunction with ICCV'98, pp. 42-51, 1998.
  25. M.J.Swain and D.H.Ballard, “Color indexing”, International Journal of Computer Vision, vol.7, no.1, pp.11-32, 1991.
  26. M.R. Naphade and T.S. Huang, “Extracting semantics from audio-visual content: the final frontier in multimedia retrieval”, IEEE Trans. on Neural Networks, vol. 13, no. 4, pp. 793–810, July 2002.
  27. M.Seetha, I.V.MuraliKrishna, B.L. Deekshatulu, “Comparison of Advanced Techniques of Image Classification”, Map World Forum Hyderabad, India.
  28. O. Chapelle, P. Haffner, and V. Vapnik, “Support vector machines for histogram-based image classification”, IEEE Transactions on Neural Networks, vol. 10, pp. 1055-1064, 1999.
  29. O. Maron and A.L. Ratan, “Multiple-Instance Learning for Natural Scene Classification”, Proceedings of the Fifteenth International Conference on Machine Learning, pp. 341-349, 1998.
  30. Rostom Kachouri, Khalifa Djemal and Hichem Maaref, Dorra Sellami Masmoudi and Nabil Derbel ,”Content description and classification for Image recognition system”, Information and Communication Technologies: From Theory to Applications, ICTTA ,3rd International Conference, April 2008.
  31. Sang-Mi Lee, Hee_Jung Bae, and Sung-Hwan Jung, “Efficient Content-Based Image Retrieval Methods Using Color and Texture”, ETRI Journal 20 (1998) 272-283.
  32. Y. Chen and J.Z. Wang, “Image Categorization by Learning and Reasoning with Regions”, Journal of Machine Learning Research, vol. 5, pp. 913-939, 2004.
  33. Zhu Xiangbin ,” Cartoon Image Classification Based on Wavelet Transform”, Asia-Pacific Conference on Information Processing, pp. 80-83,July 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Discrete Cosine Transform (DCT) Walsh Transform Image Database Transform Domain Feature Vector