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Reseach Article

Generation of an Effective Training Feature Vector using VQ for Classification of Image Database

by H. B. Kekre, Tanuja K. Sarode, Jagruti K. Save
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 108 - Number 18
Year of Publication: 2014
Authors: H. B. Kekre, Tanuja K. Sarode, Jagruti K. Save

H. B. Kekre, Tanuja K. Sarode, Jagruti K. Save . Generation of an Effective Training Feature Vector using VQ for Classification of Image Database. International Journal of Computer Applications. 108, 18 ( December 2014), 15-23. DOI=10.5120/19011-0511

@article{ 10.5120/19011-0511,
author = { H. B. Kekre, Tanuja K. Sarode, Jagruti K. Save },
title = { Generation of an Effective Training Feature Vector using VQ for Classification of Image Database },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 108 },
number = { 18 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 15-23 },
numpages = {9},
url = { },
doi = { 10.5120/19011-0511 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:43:18.792042+05:30
%A H. B. Kekre
%A Tanuja K. Sarode
%A Jagruti K. Save
%T Generation of an Effective Training Feature Vector using VQ for Classification of Image Database
%J International Journal of Computer Applications
%@ 0975-8887
%V 108
%N 18
%P 15-23
%D 2014
%I Foundation of Computer Science (FCS), NY, USA

In supervised classification of image database, feature vectors of images with known classes, are used for training purpose. Feature vectors are extracted in such a way that it will represent maximum information in minimum elements. Accuracy of classification highly depends on the content of training feature vectors and number of training feature vectors. If the number of training images increases then the performance of classification also improves. But it also leads to more storage space and computation time. The main aim of this research is to reduce the number of feature vectors in an effective way so as to reduce memory space required and computation time as well as to increase an accuracy. This paper proposes three major steps for automatic classification of image database. First step is the generation of feature vector of an image using column transform, row mean vector and fusion method. Then vector Quantization (code book size 4,8 and 16) is applied to reduce the number of training feature vectors per class and generate an effective and compact representation of them. Finally nearest neighbor classification algorithm is used as a classifier. The experiments are conducted on augmented Wang database. The results for various transforms, different similarity measures, varying sizes of feature vector, three code book sizes and different number of training images, are analyzed and compared. Results show that the proposed method increases accuracy in most of the cases.

  1. A. Vailaya, A. jain and H. Zhang. "On Image Classification: City Images vs. Landscapes. " Pattern Recognition, Published by Elsevier Science Ltd. , Vol. 31, No. 12, Dec 1998, pp. 1921-1935
  2. N. Manshor, A. R. A. Rahiman, M. Rajeswari and D. Ramchandram. "Feature Fusion in Improving Object Class Recognition. " Journal of Computer Science, Vol. 8, Issue 8, 2012, pp. 1321-1328
  3. H. Nakayama, T. Harada and Y. Kuniyoshi. "Scene Classification using Generalized Local Correlation," in Proc. of IAPR Conference on Machine Vision Applications, May-2009, Yokohama Japan, pp. 195-198
  4. D. Choudhary, A. K. Singh, S. Tiwari and V. P. Shukla. "Performance Analysis of Texture Image Classification using Wavelet Feature. " International Journal of Image, Graphics and Signal processing, Vol. 5, No. 1, Jan 2013, pp. 58-63
  5. W. H. Cho, I. S. Na, J. Y. Choi and T. H. lee. "Automatic Classification for Various Images Collections Using Two Stages Clustering Method. " Open Journal of applied sciences, Vol. 3, No. 1B, Mar 2013, pp. 47-52
  6. O. Boiman, E. Shechtman, and M. Irani, " In Defense of Nearest-Neighbor Based Image Classification, " IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2008
  7. M. W. Ashour, F. Khalid and M. A. Obaydee. "Supervised ANN classification for engg machined textures based on enhanced features extraction and reduction scheme," in Proc. of the international conference on Artificial Intelligence in Computer Science and ICT (AICS 2013), Nov 2013, Malaysia, pp. 71-80
  8. Li Fei-Fei and Pietro Perona. "A bayesian Hierarchical Model for learning natural scene categories," in Proc of IEEE conference on Computer Vision and Pattern Recognition , CVPR, Vol. 2, Jun 2005, pp. 524-531
  9. S. D. Madan Raja and A. Shanmugam. "ANN and SVM based War Scene Classification using Wavelet features: a comparitive study. " Journal of computational Information systems, Vol. 7, No. 5, 2011, pp. 1402-1411
  10. O. Brigham and R. E. Morrow. "The Fast Fourier Transform, " Spectrum, IEEE, Dec 1967, Vol. 4, Issue 12, pp. 63-70
  11. N. Ahmed, T. Natrajan and K. R. Rao. "Discrete Cosine Transform. " IEEE Transactions, Computers,Jan 1974, pp. 90-93
  12. A. K. Jain. "A Fast Karhunen-Loeve Transform for a Class of Random Processes. " IEEE Transaction on Communication, Vol. COM-24, Sep-1976, pp. 1023-1029
  13. H. B. Kekre and J. K. Solanki. "Comparitive Performanceof Various Trignometric Unitary Transforms for Transform Image Coding. " International Journal of Electronics, Vol. 44, No. 3, 1978, pp. 305-315
  14. Hartley, R. V. L. "A More Symmetrical Fourier Analysis applied to Transmission Problems, " in Proc of IRE 30, Mar-1942, pp. 144-150
  15. J. L. Walsh. "A Closed Set of Orthogonal Functions. " American Journal of Mathematics, Vol. 45, 1923, pp. 5-24
  16. H. B. Kekre and S. D. Thepade. " Image Retrieval using Non Involutional Orthogonal Kekre's Transform. " International Journal of Multidisciplinary Research And Advances in Engineering, IJMRAE, Vol. 1, No. I, Nov. 2009, pp. 189-203.
  17. E. Deza and M. Deza, "Dictionary of Distances," Elsevier, 16-Nov-2006 - 391 pages
  18. John P. , Van De Geer, "Some Aspects of Minkowski distance", Department of data theory,Leiden University. RR-95-03.
  19. S. Santini and R. Jain, "Similarity Measures," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 21, No. 9,pp. 871-883, Sept1999
  20. H. B. Kekre, T. K. Sarode and J. K. Save. "Effect of Distance Measures on Transform based Image Classification. " International Journal of Engineering Science and Technology (IJEST), Vol. 4, No. 8, Aug. 2012, pp. 3729-3742
  21. Y. Linde, A. Buzo and R. Gray. " An Algorithm for Vector Quantizer Design. " IEEE Transactions on Communications, Vol. 28, No. 1, Jan 1980, pp. 84-95
  22. R. M. Gray. " Vector Quantization. " IEEE ASSP Mag. , Apr. 1984, pp. 4-29
  23. J. Z. Wang, J. Li and G. Wiederhold. "SIMPLIcity: Semantics-sensitive Integrated Matching for Picture Libraries. " IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol 23, no. 9, 2001,pp. 947-963
Index Terms

Computer Science
Information Sciences


Supervised Classification Row Mean Vector Similarity Measures Nearest Neighbor Classifier Feature Vector.