Curvelet Based Multiclass Image Classification Under Complex Background Using Neural Network

Ajay Kumar Singh(1*), Vidya P. Shukla(2), Sangappa R. Biradar(3), Shamik Tiwari(4)

(1) FET-MITS Laxmangarh India, India
(2) Professor in Mody Institute of Technology & Science, Deemed University Lakshmangarh, India
(3) Professor in the department of Computer Science and Engineering at SDM, Dharwad, India, India
(4) Mody Institute of Technology & Science, Deemed University Laxmangarh, India
(*) Corresponding author


DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)

Abstract


Object detection and classification is very important part in images having complex background in computer vision. The objective of this work is to construct a classification system for identification of multiclass object images surrounded by complex back ground. The object images of three classes are taken from various databases for training and testing the performance of classifier. A blocking model is presented for feature extraction of the image after pre processing of the image. The curvelet features are extracted from each of the block of the image and then applied to neural network based classifier. The performance of the system is evaluated and compared by three experiments with varying block numbers.  Experimental results exhibits that method is effective and give the better results than other method of feature extraction using statistical and wavelet


Copyright © 2014 Praise Worthy Prize - All rights reserved.

Keywords


Background Removal; Curvelet; Neural Network; Blocking; Feature Extraction; Object Localization

Full Text:

PDF


References


Wood, J., Invariant pattern recognition: A review, Pattern Recognition, Vol. 29, pp 1–17,1996.

Tomas Suk and Jan Flusser, “Combined Blur and Affine Moment Invariants and their use in Pattern Recognition”, Pattern Recognition, 36, 2003.

Ajay Kumar Singh, V P Shukla, SR biradar and Shamik Tiwari, Performance analysis of wavelet and blur invariants for classification of affine and blurry images , Journal Of Theoretical And Applied Information Technology, Vol 28,3, pp 781-790, 2014.

Ajay Kumar Singh, V P Shukla, SR biradar and Shamik Tiwari, “Enhanced Performance of Multi Class Classification of Anonymous Noisy Images” , International Journal Of Image, Graphics And Signal Processing. Vol. 6, 3, PP.27-34, 2014.

Hsieh, J.W. , Automatic Traffic Surveillancesystem For Vehicle Tracking And Classification. IEEE. Trans. Intell. Trans. Syst., Vol 7 (2):pp 175-187, 2006.

Shan, Y. et al., Vehicle Identification between non-overlapping cameras without direct feature Matching. 10th IEEE. Int. Conf. Comp. Vision, pp: 1550-1558, 2005

Sun, Z. et al., 2006. Monocular precrash vehicle detection: Features and classifiers, IEEE. Trans. Image Proc., 15: 2019-2034.

Nagarajan, B. and P. Balasubramanie, 2007. Wavelet feature based neural classifier system for object classification with complex background. Iccima 07. IEEE Comp. Soc. Press, 1: 302-307, 2007

Polikar, R., The Wavelet Tutorial 2006.

Lau, H.T. and A. Al-Jumaily, Automatically Early Detection of Skin Cancer: Study Based on Neural Netwok Classification, International Conference of Soft Computing and Pattern Recognition, 2010.

Gonzalez R.C. and Woods R.E., Digital Image Processing, Prentice Hall second edn, 2007.

Jain A.K., Fundamentals of Digital Image Processing”, Prentice Hall International, 1989.

V.S. Murthy, E. Vamsidhar, J.N.V.R. Swarup Kumar and P. Sankara Rao, Content Based Image Retrieval using Hierarchical and K-Means Clustering Techniques, International Journal of Engineering Science and Technology Vol. 2 No. 3 , 2010.

E. J. Candes, L. Demanet, D. L. Donoho, Fast discrete curvelet transforms, Applied and Computational Mathematics, pp.1-43, 2005.

M. Henk Blanken, H. Ernst Blok, P. Arjen de Vries and F. Ling, Multimedia Retrieval, Heidelberg: Springer-Verlag Berlin, 2007.

Mahmoud, M.K.A., A. Al-Jumaily, and M. Takruri, eds. The Automatic Identification of Melanoma by Wavelet and Curvelet Analysis: Study Based on Neural Network Classification. 11th International Conference on Hybrid Intelligent Systems (HIS), IEEE (HIS). 680- 685, 2012.

Lau, H.T. and A. Al-Jumaily, Automatically Early Detection of Skin Cancer: Study Based on Nueral Netwok Classification , International Conference of Soft Computing and Pattern Recognition, 2011.

Freeman, J.A., Skapura, D. M. , Neural networks algorithms, applications, and programming techniques. Reading, Michigan: Addison-Wesle, 1992.

Sankar, A.B., Kumar, D., Seethalakshmi, K., Neural Network Based Respiratory Signal Classification Using Various Feed-Forward Back Propagation Training Algorithms. European Journal of Scientific Research, 2011. 49(3): p. 468-483

Ajay Kumar Singh, Shamik Twari and V P Shukla, Wavelet Based Multi Class Image Classification Using Neural Network, International Journal of Computer Applications,Vol 37, 4, 2012.

Ajay Kumar Singh, Shamik Twari and V P Shukla, An Enhancement over Texture Feature Based Multiclass Image Classification Under Unknown Noise, Broad Research in Artificial Intelligence and Neuroscience, 4, 1-4, 84-96, 2013.

Deepa S.N. and A.D. B., A survey on artificial intelligence approaches for medical image classification. Indian Journal of Science and Technology Vol 4, 11, 2011.


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2019 Praise Worthy Prize