International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 7, No. 3, 8 Feb. 2015
Cover page and Table of Contents: PDF (size: 375KB)
Wavelet Based Histogram of Oriented Gradients Feature Descriptors for Classification of Partially Occluded Objects
Full Text (PDF, 375KB), PP.54-61
Views: 0 Downloads: 0
Author(s)
Ajay Kumar Singh
1,*
V. P. Shukla
1
Shamik Tiwari
1
S. R. Biradar
2
Index Terms
HOG, WHOG, Multiclass, Occlusion, Neural Network, Wavelet
Abstract
Computer vision applications face various challenges while detection and classification of objects in real world like large variation in appearances, cluttered back ground, noise, occlusion, low illumination etc.. In this paper a Wavelet based Histogram of Oriented Gradients (WHOG) feature descriptors are proposed to represent shape information by storing local gradients in image. This results in enhanced representation of shape information. The performance of the feature descriptors are tested on multiclass image data set having partial occlusion, different scales and rotated object images. The performance of WHOG feature based object classification is compared with HOG feature based classification. The matching of test image with its learned class is performed using Back Propagation Neural Network (BPNN) algorithm. Proposed features not only performed superior than HOG but also beat wavelet, moment invariant and Curvelet.
Cite This Paper
Ajay Kumar Singh, V. P. Shukla, Shamik Tiwari, Sangappa R. Biradar, "Wavelet Based Histogram of Oriented Gradients Feature Descriptors for Classification of Partially Occluded Objects", International Journal of Intelligent Systems and Applications(IJISA), vol.7, no.3, pp.54-61, 2015. DOI:10.5815/ijisa.2015.03.07
Reference
[1]Wells, W.M. 1997. Statistical approaches to feature-based object recognition. Int. J. Computer Visision , 21(1/2):63 –98.
[2]A. Hyvarinen:. Survey on independent component analysis , Neural Computing Surveys, 2, 94-128, 1999.
[3]D.M.Gavrila,J.Giebel,andS.Munder. Vision-based pedes trian detection:the protector+system. Proc.ofthe IEEEIn-telligentVehiclesSymposium,Parma,Italy, 2004.
[4]Ajay Kumar Singh, V P Shukla, S R Biradar, Shamik tiwari, Performance analysis of wavelet & blur invariants for classification of affine and blurry images, Journal of Theoretical and Applied Information Technology, Vol 59, pp 781-790, January 2014.
[5]A. Torralba, Contextual priming for object detection, IJCV, 53(2):169–191, 2003.
[6]L. Wolf and S. Bileschi. A critical view of context. IJCV,69(2):251–261, 2006.
[7]M. Maire, S. Yu, and P. Perona. Object detection and segmentation from joint embedding of parts and pixels. In ICCV, 2011.
[8]M. Blaschko and C. Lampert. Object localization with global and local context kernels. In BMVC , 2009.
[9]Dalal, N. and Triggs, B., “Histograms of Oriented Gradients for Human Detection,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, San Diego, CA, USA
[10]S. A. Nene, S. K. Nayar and H. Murase, Columbia Object Image Library (COIL-100), Technical Report CUCS-006-96, February 1996.
[11]R. C. Gonzalez, R. E. Woods, "Digital Image Processing third edition", Prentice Hall, 2008.
[12]S. Mallat, “A theory for multiresolution signal decomposition: The wavelet representation,” IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp. 674–693, 1989.
[13]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)2012, IEEE (HIS). 680 685.
[14]Lau, H.T. and A. Al-Jumaily, Automatically Early Detection of Skin Cancer: Study Based on Nueral Netwok Classification , in International Conference of Soft Computing and Pattern Recognition2011.
[15]Freeman, J.A., Skapura, D. M. , Neural networks algorithms, applications, and programming techniques. Reading, Michigan: Addison-Wesle, 1992.
[16]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
[17]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. 6,3, PP.27-34, 2014.
[18]Ajay Kumar Singh, Shamik Twari and V P Shukla, “Wavelet based multi class image classification using neural network”, International Journal of Computer Applications, 37, 4, 2012.
[19]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, 2013, 84-96.
[20]Deepa S.N. and A.D. B., A survey on artificial intelligence approaches for medical image classification. Indian Journal of Science and Technology 2011. 4 (11).
[21]S. Nene, S. Nayar, and H. Murase (1996). Columbia object image library (COILl-100). Dept. Comput. Sci., Columbia Univ., New York, Tech. Rep. CUCS-006-96.
[22]Ajay Kumar Singh, V P Shukla, SR biradar and Shamik Tiwari, “Curvelet Based Multiclass Image Classification under Complex Background Using Neural Network”, International Review on Computer and Software, March 2014 issue.