Call for Paper - January 2024 Edition
IJCA solicits original research papers for the January 2024 Edition. Last date of manuscript submission is December 20, 2023. Read More

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

International Conference and Workshop on Emerging Trends in Technology
© 2011 by IJCA Journal
Number 4 - Article 3
Year of Publication: 2011
H.B. Kekre
Tanuja K. Sarode
Meena S. Ugale

H B Kekre, Tanuja Sarode and Meena S Ugale. Performance Comparison of Image Classifier using Discrete Cosine Transform and Walsh Transform. IJCA Proceedings on International Conference and workshop on Emerging Trends in Technology (ICWET) (4):14-20, 2011. Full text available. BibTeX

	author = {H. B. Kekre and Tanuja Sarode and Meena S. Ugale},
	title = {Performance Comparison of Image Classifier using Discrete Cosine Transform and Walsh Transform},
	journal = {IJCA Proceedings on International Conference and workshop on Emerging Trends in Technology (ICWET)},
	year = {2011},
	number = {4},
	pages = {14-20},
	note = {Full text available}


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.

Image Classification refers to grouping of a digital image into different classes within a particular dataset, based on attribute values. It is done to replace visual analysis of the image data with quantitative techniques.

This paper presents the image classification techniques based on feature vectors of transformed images using Discrete Cosine Transform and Walsh Transform. The various sizes of feature vectors are generated such as 8X8, 16X16, 32X32, 64X64 and 128X128. The proposed algorithm is worked over database of 1000 images spread over 10 different classes. The Euclidean distance is used as similarity measure. A threshold value is set to determine to which category the query image belongs to.


  • A.K.Jain and A.Vailaya, “Image retrieval using color and shape”, Pattern recognition, vol.29, no.8, pp.1233-1244, 1996.
  • 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.
  • D. J. Le Gall, “The MPEG Video Compression Algorithm: A review,” SPIE 1452 (1991) 444-457.
  • 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.
  • 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.
  • 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.
  • Emma Newham, “The biometric report,” SJB Services, 1995.
  • 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.
  • F.Mokhtarian and S.Abbasi, “Shape similarity retrieval under affine transforms”, Pattern Recognition, 2002, vol. 35, pp. 31-41.
  • 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
  • G. Griffin, A. Holub, and P. Perona. Caltech 256 objects category dataset. Technical Report UCB/CSD-04-1366, California Institute of Technology, 2007.
  • G. K. Wallace, “Overview of the JPEG still Image Compression standard,” SPIE 1244 (1990) 220-233.
  • Golam sorwar, Ajith abraham, “DCT based texture classification using soft computing approach”, Malaysian Journal of Computer Science, vol. 17,2004
  • 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..
  • 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.
  • 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).
  • 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.
  • 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).
  • 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
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • M.J.Swain and D.H.Ballard, “Color indexing”, International Journal of Computer Vision, vol.7, no.1, pp.11-32, 1991.
  • 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.
  • M.Seetha, I.V.MuraliKrishna, B.L. Deekshatulu, “Comparison of Advanced Techniques of Image Classification”, Map World Forum Hyderabad, India.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Zhu Xiangbin ,” Cartoon Image Classification Based on Wavelet Transform”, Asia-Pacific Conference on Information Processing, pp. 80-83,July 2009.