Wavelet - Based Classification of Outdoor Natural Scenes by Resilient Neural Network
Natural outdoor scene classification is active and
promising research area around the globe. In this study, the
classification is carried out in two phases. In the first phase, the
features are extracted from the images by wavelet decomposition
method and stored in a database as feature vectors. In the second
phase, the neural classifiers such as back-propagation neural network
(BPNN) and resilient back-propagation neural network (RPNN) are
employed for the classification of scenes. Four hundred color images
are considered from MIT database of two classes as forest and street.
A comparative study has been carried out on the performance of the
two neural classifiers BPNN and RPNN on the increasing number of
test samples. RPNN showed better classification results compared to
BPNN on the large test samples.
[1] Ajay Kumar, "Neural Network Based Detection of Local Textile
Defects,” Pattern Recognition 36, pp. 1645-1659, 2003.
[2] Andrew and Sameer Singh, "Indoor vs. outdoor scene classification in
digital photographs,” Pattern Recognition 38, pp. 1533-1545, 2005.
[3] Chien-Sheng Chen and Jium-Ming Lin, " ApplyingRprop Neural
Network for the Prediction of the Mobile Station Location,” Sensors
(Open Access), pp.4207-4230, 2011.
[4] C. K. Chui, Wavelets-A Mathematical tool for Signal Processing, SIAM,
Philadelphia, 1997.
[5] http://cvcl.mit.edu/database.htm
[6] I. Daubechies, "Ten Lectures on Wavelets ,” SIAM CBMS-NSF, Series
on Applied Mathematics, no. 61, pp. 53-166, SIAM, 1992.
[7] H. Demuth and H. Mark eds. 2002, The Matlab version 7.0 : User
Guide, The Math Works Inc., USA.
[8] GeethaSrikantan, Stephen W. Lam and Sargur N. Srihari, "Gradient-
Based Contour Encoding For Character Recognition,” Pattern
Recognition, Vol. 79, No. 7, pp. 1147-1160, 1996.
[9] A. Guerin-Dugue, A. Olivia, "Classification of scene photographs from
local orientations features,” Pattern Recognition Lett. 21, pp. 1135-1140,
2000.
[10] S. Haykin, Neural Networks: a Comprehensive Foundation, Macmillan,
New York, USA, 2010.
[11] Lalit Gupta, V. Pathangay, A. Patra A. Dyana and Sukhendu Das,
"Indoor Vs. Outdoor Scene Classification using Probabilistic Neural
Network,” EURASIP Journal on Advances in Signal Processing Special
Issueon Image Perceptions, Vol. 2007, pp. 1-10, 2007.
[12] S. G. Mallat, "A Theory of Multiresolution Signal Decomposition : The
Wavelet Representation,” IEEE Transactions on Pattern Recognition and
Machine Intelligence 11, pp.674-693, 1989a.
[13] Matthew Traherne and Sameer Singh, "An Integrated Approach to
Automatics Indoor Outdoor Scene Classification In Digital Images,”
Proceedings of 5th International Conference on Intelligent Data
engineering Automated Learning (IDEAL), Exeter, UK, 2004.
[14] D. Mic˘us˘i´k, V. Stopjakova´ and L. Ben˘us˘kova, "Application of
Feed-forward Artificial Neural Networks to the Identification of
Defective Analog Integrated Circuits,” Neural Computation and
Applications, pp. 71-79, 2002.
[15] T. Mitchell, Machine Learning, McGraw Hill, 1997.
[16] Mohammed A. Ayoub, Birol M. Demiral, "Application of Resilient
Back-Propagation Neural Networks for Generating a Universal Pressure
Drop Model in Pipelines,” University of Khartoum Engineering Journal,
Vol. 1, Issue 2 pp. 9-21, October 2011.
[17] ]G. Nathalie, L.B. Herve, H. Jeanny, G.D. Anne, "Towards the
Introduction of Human Perception in a Natural Scene Classification
System, IEEE Conference NNSP, Switzerland, September 4–6, 2002.
[18] OzgurKisi and ErdalUncuoglu, "Comparison of three-backpropogation
training algorithms,” Indian Journal of engineering and Material
Sciences, Vol. 12, pp. 434-442, October 2005.
[19] L. M. Patnaik and K. Rajan, "Target detection through image processing
and resilient propagation algorithms,” Neurocomputing, pp. 123–135,
2000.
[20] Reyadh Shaker Naoum and ZainabNamh Al-Sultani, " Hybrid System
Of Learning Vector Quantization And Enhanced Resilient
Backpropagation Artificial Neural Network For Intrusion
Classification,” IJRRAS 14 (2), pp. 333-339, February 2013.
[21] M. Riedmiller and H. Braun, "A direct adaptive method for faster
backpropagation learning: The RPROP algorithm,” Proc. IEEE Int.
Conf. On Neural Network, pp. 586-591, 1993.
[22] E. Saber and A. M. Tekalp, "Integration of color, edge, shape and
texture features for automatic region-based image annotation and
retrieval,” Electronics Imaging, vol. 7, pp. 684-700, 1998.
[23] Son Lam Phung and A. Bouzerdoum, "A Pyramidal Neural Network For
Visual Pattern Recognition,” IEEE Transactions On Neural Networks,
Vol. 18, No. 2, pp.329-343, March 2007.
[24] S. Srinivasan, L. Kanal, " Qualitative Landmark Recognition and using
Visual cues,” Pattern Recognition Lett. 18, pp. 1405-1414, 1997.
[25] G. SubramanyaNayak and DayanandaNayak, "Classification of ECG
Signals using ANN with Resilient Back Propagation Algorithm,”
International Journal of Computer Applications Volume 54, No.6, pp.
20-24, September 2012.
[26] B. Yegnanarayana, Artificial Neural Networks, PHI Learning Pvt. Ltd.,
2009.
[1] Ajay Kumar, "Neural Network Based Detection of Local Textile
Defects,” Pattern Recognition 36, pp. 1645-1659, 2003.
[2] Andrew and Sameer Singh, "Indoor vs. outdoor scene classification in
digital photographs,” Pattern Recognition 38, pp. 1533-1545, 2005.
[3] Chien-Sheng Chen and Jium-Ming Lin, " ApplyingRprop Neural
Network for the Prediction of the Mobile Station Location,” Sensors
(Open Access), pp.4207-4230, 2011.
[4] C. K. Chui, Wavelets-A Mathematical tool for Signal Processing, SIAM,
Philadelphia, 1997.
[5] http://cvcl.mit.edu/database.htm
[6] I. Daubechies, "Ten Lectures on Wavelets ,” SIAM CBMS-NSF, Series
on Applied Mathematics, no. 61, pp. 53-166, SIAM, 1992.
[7] H. Demuth and H. Mark eds. 2002, The Matlab version 7.0 : User
Guide, The Math Works Inc., USA.
[8] GeethaSrikantan, Stephen W. Lam and Sargur N. Srihari, "Gradient-
Based Contour Encoding For Character Recognition,” Pattern
Recognition, Vol. 79, No. 7, pp. 1147-1160, 1996.
[9] A. Guerin-Dugue, A. Olivia, "Classification of scene photographs from
local orientations features,” Pattern Recognition Lett. 21, pp. 1135-1140,
2000.
[10] S. Haykin, Neural Networks: a Comprehensive Foundation, Macmillan,
New York, USA, 2010.
[11] Lalit Gupta, V. Pathangay, A. Patra A. Dyana and Sukhendu Das,
"Indoor Vs. Outdoor Scene Classification using Probabilistic Neural
Network,” EURASIP Journal on Advances in Signal Processing Special
Issueon Image Perceptions, Vol. 2007, pp. 1-10, 2007.
[12] S. G. Mallat, "A Theory of Multiresolution Signal Decomposition : The
Wavelet Representation,” IEEE Transactions on Pattern Recognition and
Machine Intelligence 11, pp.674-693, 1989a.
[13] Matthew Traherne and Sameer Singh, "An Integrated Approach to
Automatics Indoor Outdoor Scene Classification In Digital Images,”
Proceedings of 5th International Conference on Intelligent Data
engineering Automated Learning (IDEAL), Exeter, UK, 2004.
[14] D. Mic˘us˘i´k, V. Stopjakova´ and L. Ben˘us˘kova, "Application of
Feed-forward Artificial Neural Networks to the Identification of
Defective Analog Integrated Circuits,” Neural Computation and
Applications, pp. 71-79, 2002.
[15] T. Mitchell, Machine Learning, McGraw Hill, 1997.
[16] Mohammed A. Ayoub, Birol M. Demiral, "Application of Resilient
Back-Propagation Neural Networks for Generating a Universal Pressure
Drop Model in Pipelines,” University of Khartoum Engineering Journal,
Vol. 1, Issue 2 pp. 9-21, October 2011.
[17] ]G. Nathalie, L.B. Herve, H. Jeanny, G.D. Anne, "Towards the
Introduction of Human Perception in a Natural Scene Classification
System, IEEE Conference NNSP, Switzerland, September 4–6, 2002.
[18] OzgurKisi and ErdalUncuoglu, "Comparison of three-backpropogation
training algorithms,” Indian Journal of engineering and Material
Sciences, Vol. 12, pp. 434-442, October 2005.
[19] L. M. Patnaik and K. Rajan, "Target detection through image processing
and resilient propagation algorithms,” Neurocomputing, pp. 123–135,
2000.
[20] Reyadh Shaker Naoum and ZainabNamh Al-Sultani, " Hybrid System
Of Learning Vector Quantization And Enhanced Resilient
Backpropagation Artificial Neural Network For Intrusion
Classification,” IJRRAS 14 (2), pp. 333-339, February 2013.
[21] M. Riedmiller and H. Braun, "A direct adaptive method for faster
backpropagation learning: The RPROP algorithm,” Proc. IEEE Int.
Conf. On Neural Network, pp. 586-591, 1993.
[22] E. Saber and A. M. Tekalp, "Integration of color, edge, shape and
texture features for automatic region-based image annotation and
retrieval,” Electronics Imaging, vol. 7, pp. 684-700, 1998.
[23] Son Lam Phung and A. Bouzerdoum, "A Pyramidal Neural Network For
Visual Pattern Recognition,” IEEE Transactions On Neural Networks,
Vol. 18, No. 2, pp.329-343, March 2007.
[24] S. Srinivasan, L. Kanal, " Qualitative Landmark Recognition and using
Visual cues,” Pattern Recognition Lett. 18, pp. 1405-1414, 1997.
[25] G. SubramanyaNayak and DayanandaNayak, "Classification of ECG
Signals using ANN with Resilient Back Propagation Algorithm,”
International Journal of Computer Applications Volume 54, No.6, pp.
20-24, September 2012.
[26] B. Yegnanarayana, Artificial Neural Networks, PHI Learning Pvt. Ltd.,
2009.
@article{"International Journal of Information, Control and Computer Sciences:60427", author = "Amitabh Wahi and Sundaramurthy S.", title = "Wavelet - Based Classification of Outdoor Natural Scenes by Resilient Neural Network", abstract = "Natural outdoor scene classification is active and
promising research area around the globe. In this study, the
classification is carried out in two phases. In the first phase, the
features are extracted from the images by wavelet decomposition
method and stored in a database as feature vectors. In the second
phase, the neural classifiers such as back-propagation neural network
(BPNN) and resilient back-propagation neural network (RPNN) are
employed for the classification of scenes. Four hundred color images
are considered from MIT database of two classes as forest and street.
A comparative study has been carried out on the performance of the
two neural classifiers BPNN and RPNN on the increasing number of
test samples. RPNN showed better classification results compared to
BPNN on the large test samples.
", keywords = "BPNN, Classification, Feature extraction, RPNN,
Wavelet.", volume = "8", number = "9", pages = "1625-4", }
