Multi Spectral Inter-Correlative Approach for Feature Selection in Pattern Recognition

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2017 by IJETT Journal
Volume-43 Number-3
Year of Publication : 2017
Authors : ShaikhAfroz Fatima Muneeruddin, Dr. Nagraj B.Patil
DOI :  10.14445/22315381/IJETT-V43P227


ShaikhAfroz Fatima Muneeruddin, Dr. Nagraj B.Patil "Multi Spectral Inter-Correlative Approach for Feature Selection in Pattern Recognition", International Journal of Engineering Trends and Technology (IJETT), V43(3),162-168 January 2017. ISSN:2231-5381. published by seventh sense research group

Feature representing is a prime requirement in the pattern recognition area, where features are represented in transformed domain to get finer resolution description of image representation. In the spectral oriented feature representation, wavelet based coding are more dominantly been used for its finer detail feature representiaon. The spectral feature representation based on wavelet transformation, are more informative, however the feature counts are of large count resulting in processing overhead. Towards improving the recognition efficiency, in this paper a new coding approach based on band correlation is presented, which minimizes the number of selective band coefficients, minimize the processing overhead.


1] Te-Wei Chiang, Tien-Wei Tsai, “Content-Based Image Retrieval via the Multiresolution Wavelet Features of Interest”, Journal of Information Technology and Applications Vol. 1 No. 3, December, 2006.
[2] Vinayak D. Shinde, Vijay M. Mane, “ Pattern Recognition using Multilevel Wavelet Transform”, International Journal of Computer Applications, Volume 49– No.2, July 2012.
[3] ZhongliangLuo, “Iris Feature Extraction and Recognition Based on Wavelet-Based Contourlet Transform”, Procedia Engineering, Elsevier, 2012.
[4] Mathieu Lamard, Guy Cazuguel, Gw_enoleQuellec, Lynda Bekri, Christian Roux, “Content Based Image Retrieval based on Wavelet Transform coefficients distribution”, Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society., IEEE, 2007.
[5] Hamid Soltanian-Zadeha, FarshidRafiee-Rad, SiamakPourabdollah-Nejad D, “Comparison of multiwavelet, wavelet, Haralick, and shape features for micro calcification classification in mammograms”, journal of Pattern Recognition, Pergamon, Elsevier, 2004.
[6] Varun Bajaj, Ram BilasPachori, “Classification of human emotions based on multiwavelet transform of EEG signals”, AASRI Procedia, Elsevier, 2012.
[7] Jomy John, Pramod K.V., KannanBalakrishnan, “Unconstrained Handwritten Malayalam Character Recognition using Wavelet Transform and Support vector Machine Classifier”, Procedia Engineering, Elsevier, 2012.
[8] Strela V., Heller P., Strang G., Topiwala P., and Heil Ch., “The Application of Multiwavelet Filter Banks to Image Processing,” IEEE Transactions on Image Processing, Georgia Institute of Technology, Atlanta, 2002.
[9] Janaki. R, Tamilarasi, “Visually Improved Image Compression by using Embedded Zero-tree Wavelet Coding”, IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 2, March 2011.
[10]B.Nassiri, R.Latif, “Study of Wavelet Based Medical Image Compression Techniques”, International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 3, Issue 3, May 2014.
[11]Lalitha Y. S, “Image Compression of MRI Image using Planar Coding”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No. 7, 2011.
[12]Kong Aik, WoonSengGanNanyang ,Sen M. Kuo, “Subband Adaptive Filtering Theory and Implementation”, John Wiley and Sons, Ltd, 2009.
[13]Moon-Kyu Song, Seong-Eun Kim, Young-Seok Choi, and Woo-Jin Song, “A Selective Normalized Subband Adaptive Filter Exploiting an efficient subset of Sub bands”, EURASIP, 2011.
[14] Mohammad Shams EsfandAbadi, and Mohammad SaeedShafiee, “A New Variable Step-Size Normalized Subband Adaptive Filter Algorithm Employing Dynamic Selection of Subband Filters”, IEEE,2013.

transformation, pattern recognition, inter-correlative coding, and feature selection approach.