Wavelet Based Histogram of Oriented Gradients Feature Descriptors for Classification of Partially Occluded Objects

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Ajay Kumar Singh 1,* V. P. Shukla 1 Shamik Tiwari 1 S. R. Biradar 2

1. Faculty of Engineering & Technology, Mody University of Science & Technology, Lakshmangarh, Sikar, India

2. Dept. of Computer Sc. & Engg., SDM college of Engg. Dharwad, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2015.03.07

Received: 22 Jul. 2014 / Revised: 4 Oct. 2014 / Accepted: 26 Nov. 2014 / Published: 8 Feb. 2015

Index Terms

HOG, WHOG, Multiclass, Occlusion, Neural Network, Wavelet


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


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