International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 7, No. 3, 8 Feb. 2015
Cover page and Table of Contents: PDF (size: 1010KB)
A Comparative Study of Feature Extraction Methods in Images Classification
Full Text (PDF, 1010KB), PP.16-23
Views: 0 Downloads: 0
Author(s)
Index Terms
Feature extraction, Image classification, Models evaluation, Support vector machine
Abstract
Feature extraction is an important step in image classification. It allows to represent the content of images as perfectly as possible. However, in this paper, we present a comparison protocol of several feature extraction techniques under different classifiers. We evaluate the performance of feature extraction techniques in the context of image classification and we use both binary and multiclass classifications. The analyses of performance are conducted in term of: classification accuracy rate, recall, precision, f-measure and other evaluation measures. The aim of this research is to show the relevant feature extraction technique that improves the classification accuracy rate and provides the most implicit classification data. We analyze the models obtained by each feature extraction method under each classifier.
Cite This Paper
Seyyid Ahmed Medjahed,"A Comparative Study of Feature Extraction Methods in Images Classification", IJIGSP, vol.7, no.3, pp.16-23, 2015. DOI: 10.5815/ijigsp.2015.03.03
Reference
[1]S. Amin Seyyedi, N. Ivanov,"Statistical Image Classification for Image Steganographic Techniques", International Journal of Image, Graphics and Signal Processing (IJIGSP), vol. 6, no. 8, pp. 19-24, 2014.DOI: 10.5815/ijigsp.2014.08.03.
[2]D. Choudhary, A. K. Singh, S. Tiwari,V. P. Shukla,"Performance Analysis of Texture Image Classification Using Wavelet Feature", International Journal of Image, Graphics and Signal Processing (IJIGSP),, vol. 5, no. 1, pp. 58-63, 2013.DOI: 10.5815/ijigsp.2013.01.08.
[3]Z. Akata, F. Perronnin, Z. Harchaoui and C. Schmid, "Good Practice in Large-Scale Learning for Image Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013.
[4]S. Maji, A. C. Berg and J. Malik, "Efficient Classification for Additive Kernel SVMs," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 1, pp. 66-77, 2013.
[5]M. Luo and K. Zhang, "A hybrid approach combining extreme learning machine and sparse representation for image classification", Engineering Applications of Artificial Intelligence, vol. 27, no. 1, pp. 228-235, 2014.
[6]A. Morales-González, N. Acosta-Mendoza, A. Gago-Alonso, E. B. García-Reyes and J. E. Medina-Pagola, "A new proposal for graph-based image classification using frequent approximate subgraphs", Pattern Recognition, vol. 47, no. 1, pp. 169-177, 2014.
[7]R. J. Mullen, D. N. Monekosso and P. Remagnino, "Ant algorithms for image feature extraction", Expert Systems with Applications, vol. 40, no. 11, pp. 4315-4332, 2013.
[8]J. Qian, J. Yang and G. Gao, "Discriminative histograms of local dominant orientation (D-HLDO) for biometric image feature extraction", Pattern Recognition, vol. 46, no. 10, pp. 2724-2739, 2013.
[9]K. Xiao, A. L. Liang, H. B. Guan and A. E. Hassanien, "Extraction and application of deformation-based feature in medical images", Neurocomputing, vol. 120, pp. 177-184, 2013.
[10]C. Li, Q. Liu, J. Liu and H. Lu, "Ordinal regularized manifold feature extraction for image ranking", Signal Processing, vol. 93, no. 6, pp. 1651-1661, 2013.
[11]P. L. Stanchev, D. Green Jr. and B. Dimitrov. "High level colour similarity retrieval", International Journal of Information Theories and Applications, vol. 10, no. 3, pp. 363-369, 2003.
[12]J. Huang, S. Kuamr, M. Mitra, W. Zhu et al., "Image indexing using colour correlogram", In Proc. Computer Vision and Pttern Recognition, pp. 762-765, 1997.
[13]M. K. Swain and D. H. Ballard, "Color indexing". International Journal of Computer Vision, vol. 7, no. 1, pp. 11-32, 1991.
[14]D. P. Tian and B. Shaanxi, "A Review on Image Feature Extraction and Representation", Techniques International Journal of Multimedia and Ubiquitous Engineering, vol. 8, no. 4, pp. 385-396, 2013
[15]L. Li and P. W. Fieguth, "Texture Classification from Random Features," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 3, pp. 574-586, 2012.
[16]T. Yu, V. Muthukkumarasamy, B. Verma and M. Blumenstein, "A Texture Feature Extraction Technique Using 2D-DFT and Hamming Distance,", Fifth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'03), 2003.
[17]D. Zhang and G. Lu, "Review of shape representation and description techniques", Pattern Recognition, vol. 37, no. 1, pp. 1-19, 2004.
[18]C. Cortes and V. Vapnik, "Support-vector networks," Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
[19]P. Bartlett and J. Shawe-taylor, "Generalization performance of support vector machines and other pattern classifiers," Advances in Kernel Methods - Support Vector Learning, pp. 43-54, 1998.
[20]J. Ye and T. Xiong, "Svm versus least squares svm," Journal of Machine Learning Research, vol. 2, pp. 644-651, 2007.
[21]J. C. Platt, "Improvements to platt's smo algorithm for svm classifier design," Neural Computation, vol. 13, no. 3, pp. 637-649, 2001.
[22]J. C. Platt, B. Schölkopf, C. Burges, and A. Smola, "Fast training of support vector machines using sequential minimal optimization," Advances in Kernel Methods - Support Vector Learning, pp. 185-208, 1999.
[23]G. W. Flake and S. Lawrence, "Efficient svm regression training with smo," Journal Machine Learning, vol. 46, no. 3, pp. 271-290, 2002.
[24]J. A. K. Suykens, T. V. Gestel, J. D. Brabanter, B. D. Moor, and J. Vandewalle, "Least squares support vector machines," World Scientific Pub. Co., Singapore, 2002.
[25]J. A. K. Suykens and J. Vandewalle, "Least squares support vector machine classifiers," Neural Processing Letters, vol. 9, no. 3, pp. 293-300, 1999.
[26]L. Jiao, L. Bo, and L. Wang, "Fast sparse approximation for least squares support vector machine," IEEE Transactions on Neural Transactions, vol. 19, no. 3, pp. 685-697, 2007.
[27]B. Anna, Z. Andrew, and M. Xavier, "Representing shape with a spatial pyramid kernel," CIVR '07 Proceedings of the 6th ACM international conference on Image and video retrieval, 2007.
[28]D. Mery, "Classification of Potential Defects in Automated Inspection of Aluminium Castings Using Statistical Pattern Recognition", In Proceedings of 8th European Conference on Non-Destructive Testing (ECNDT 2002) Barcelona, Spain, pp. 17-21, 2002.
[29]A. Fitzgibbon, M. Pilu and R. B. Fisher, "Direct Least Square Fitting Ellipses", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 5, pp. 476-480, 1999.
[30]W. D. Stromberg and T. G. Farr, "A Fourier-Based Textural Feature Extraction Procedure", IEEE Transactions on Geoscience and Remote Sensing, vol. 25, no. 5, pp. 722 – 731, 1986.
[31]C. Zahn and R. Roskies, "Fourier Descriptors for Plane Closed Curves", IEEE Transactions on Computers, vol. 21, no. 3, pp. 269-281, 1972.
[32]G. Zhang, Z. Ma, L. Niu and C. Zhang, "Modified Fourier descriptor for shape feature extraction", Journal of Central South University, vol. 19, no. 2, pp. 488-495, 2012.
[33]A. Kumar and G. K. H. Pang, "Defect detection in textured materials using Gabor filters", IEEE Transactions on Industry Applications, vol. 38, no. 2, pp. 425-440, 2002.
[34]L. Shen and L. Bai, "Gabor Feature Based Face Recognition Using Kernel Methods", Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004.
[35]L. Gupta and M. D. Srinath, "Contour sequence moments for the classification of closed planar shapes", Pattern Recognition, vol. 20, pp. 267-272, 1987.
[36]Haralick, "Statistical and Structural Approaches to Texture", Proc. IEEE, vol. 67, no. 5, pp. 786-804, 1979.
[37]A. Porebski, N. Vandenbroucke and L. Macaire, "Haralick feature extraction from LBP images for color texture classification", Published in: Image Processing Theory, Tools and Applications, pp. 1-8, 2008.
[38]M. K. Hu, "Visual Pattern Recognition by Moment Invariants", IRE Transaction on Information Theory IT-8, pp. 179-187, 1962.
[39]M. Mercimek, K. Gulez and T. V. Mumcu, "Real object recognition using moment invariants", Sadhana, vol. 30, no. 6, pp. 765–775, 2005
[40]P. Bhaskara Rao, D.Vara Prasad and Ch.Pavan Kumar, "Feature Extraction Using Zernike Moments", International Journal of Latest Trends in Engineering and Technology, vol. 2, no. 2, pp. 228-234, 2013.
[41]T. Ojala, M. Pietikainen and T. Maenpaa, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, pp. 971-987, 2002.
[42]Mu, Y. et al, "Discriminative Local Binary Patterns for Human Detection in Personal Album", CVPR-2008, 2008.
[43]T. Ahonen, A. Hadid and M. Pietikinen, "Face Description with Local Binary Patterns: Application to Face Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037-2041, 2006.
[44]R. J. Ramteke, "Invariant Moments Based Feature Extraction for Handwritten Devanagari Vowels Recognition", International Journal of Computer Applications, vol. 1, no. 18, pp. 1-5, 2010.
[45]L. Fei-Fei, R. Fergus and P. Perona, "Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories", IEEE. CVPR 2004, Workshop on Generative-Model Based Vision, 2004.
[46]D. Mery, "BALU: A toolbox Matlab for computer vision, pattern recognition and image processing", http://dmery.ing.puc.cl/index.php/balu,2011.
[47]M. Kubat, R. Holte and S. Matwin, "Learning when negative examples aboud", In ECML-97, pp. 146-153, 1997.
[48]N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection", Proceedings of the Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886-893, 2005.
[49]H. Zhang and Z. Sha, "Product Classification based on SVM and PHOG Descriptor", International Journal of Computer Science and Network Security, vol. 13, no. 9, pp. 1-4, 2013.