An approach towards wavelet transform based multiclass object classification

Object classification is an important problem in computer vision, in which multiclass object classification is more difficult one in comparison to single class object classification. In this paper, we proposed a new method for multiclass object classification based on discrete wavelet transform. We have used discrete wavelet transform coefficients as a feature of object, because of its multi-resolution property. We have used multiclass support vector machine as a classifier for classification of objects. The proposed method has been tested on own dataset prepared by authors of this paper. We have tested the proposed method on multiple levels of discrete wavelet transform. Quantitative evaluation results shows that the proposed method gives better performance for multiclass object classification at higher level of discrete wavelet transform and other state-of-the-art methods.

[1]  Ashish Khare,et al.  Object Tracking of Video Sequences in Curvelet Domain , 2011, Int. J. Image Graph..

[2]  Om Prakash,et al.  Adaptive real-time motion segmentation technique based on statistical background model , 2014 .

[3]  Tieniu Tan,et al.  Recent developments in human motion analysis , 2003, Pattern Recognit..

[4]  Manish Khare,et al.  Dual Tree Complex Wavelet Transform Based Video Object Tracking , 2010, ICT.

[5]  Sergio A. Velastin,et al.  Object classification for real-time video-surveillance applications , 2008 .

[6]  Manish Khare,et al.  Curvelet transform based moving object segmentation , 2013, 2013 IEEE International Conference on Image Processing.

[7]  Ashish Khare,et al.  Soft-Thresholding for Denoising of Medical Images - a Multiresolution Approach , 2005, Int. J. Wavelets Multiresolution Inf. Process..

[8]  Tieniu Tan,et al.  Real-Time Moving Object Classification with Automatic Scene Division , 2007, 2007 IEEE International Conference on Image Processing.

[9]  M. Hanna,et al.  The discrete time wavelet transform: its discrete time Fourier transform and filter bank implementation , 2001 .

[10]  Raanan Yehezkel,et al.  Multiclass object classification for real-time video surveillance systems , 2011, Pattern Recognit. Lett..

[11]  Ashish Khare,et al.  Rule based human activity recognition for surveillance system , 2012, 2012 4th International Conference on Intelligent Human Computer Interaction (IHCI).

[12]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[13]  Yi Lin.  Multicategory Support Vector Machines, Theory, and Application to the Classification of . . . , 2003 .

[14]  Mubarak Shah,et al.  Tracking and Object Classification for Automated Surveillance , 2002, ECCV.

[15]  Sebastian Nowozin,et al.  Object Classification using Local Image Features , 2006 .

[16]  Jean-Jacques E. Slotine,et al.  FastWavelet-Based Visual Classification , 2008, 2008 19th International Conference on Pattern Recognition.

[17]  Shahram Shirani,et al.  A robust multimedia watermarking technique using Zernike transform , 2001, 2001 IEEE Fourth Workshop on Multimedia Signal Processing (Cat. No.01TH8564).

[18]  Tieniu Tan,et al.  A survey on visual surveillance of object motion and behaviors , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[19]  Nicolás García-Pedrajas,et al.  Improving multiclass pattern recognition by the combination of two strategies , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Manish Khare,et al.  Moving object segmentation in Daubechies complex wavelet domain , 2015, Signal Image Video Process..