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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  4/2016 - 16
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 HIGH-IMPACT PAPER 

An Efficient Method of HOG Feature Extraction Using Selective Histogram Bin and PCA Feature Reduction

LAI, C. Q. See more information about LAI, C. Q. on SCOPUS See more information about LAI, C. Q. on IEEExplore See more information about LAI, C. Q. on Web of Science, TEOH, S. S. See more information about TEOH, S. S. on SCOPUS See more information about TEOH, S. S. on SCOPUS See more information about TEOH, S. S. on Web of Science
 
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Download PDF pdficon (1,987 KB) | Citation | Downloads: 1,221 | Views: 3,889

Author keywords
feature extraction, image analysis, object detection, pattern recognition, computer vision

References keywords
detection(18), vision(9), pattern(9), human(8), pedestrian(7), recognition(6), feature(6), cvpr(6), oriented(5), histogram(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2016-11-30
Volume 16, Issue 4, Year 2016, On page(s): 101 - 108
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2016.04016
Web of Science Accession Number: 000390675900016
SCOPUS ID: 85007569629

Abstract
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Histogram of Oriented Gradient (HOG) is a popular image feature for human detection. It presents high detection accuracy and therefore has been widely used in vision-based surveillance and pedestrian detection systems. However, the main drawback of this feature is that it has a large feature size. The extraction algorithm is also computationally intensive and requires long processing time. In this paper, a time-efficient HOG-based feature extraction method is proposed. The method uses selective number of histogram bins to perform feature extraction on different regions in the image. Higher number of histogram bin which can capture more detailed information is performed on the regions of the image which may belong to part of a human figure, while lower number of histogram bin is used on the rest of the image. To further reduce the feature size, Principal Component Analysis (PCA) is used to rank the features and remove some unimportant features. The performance of the proposed method was evaluated using INRIA human dataset on a linear Support Vector Machine (SVM) classifier. The results showed the processing speed of the proposed method is 2.6 times faster than the original HOG and 7 times faster than the LBP method while providing comparable detection performance.


References | Cited By

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Cited-By SCOPUS

SCOPUS® Times Cited: 16
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Cited-By CrossRef

[1] Performance of Interpolated Histogram of Oriented Gradients on the Feature Calculation of SIFT, OZTURK, A., CAYIROGLU, I., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 3, Volume 22, 2022.
Digital Object Identifier: 10.4316/AECE.2022.03010
[CrossRef] [Full text]

[2] Triple-feature-based Particle Filter Algorithm Used in Vehicle Tracking Applications, ABDULLA, A. A., GRAOVAC, S., PAPIC, V., KOVACEVIC., B., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 2, Volume 21, 2021.
Digital Object Identifier: 10.4316/AECE.2021.02001
[CrossRef] [Full text]

[3] Automatic Switching of Electric Locomotive Power in Railway Neutral Sections Using Image Processing, Mcineka, Christopher Thembinkosi, Pillay, Nelendran, Moorgas, Kevin, Maharaj, Shaveen, Journal of Imaging, ISSN 2313-433X, Issue 6, Volume 10, 2024.
Digital Object Identifier: 10.3390/jimaging10060142
[CrossRef]

[4] A computer-aided diagnosis system for brain tumors in magnetic resonance imaging (MRI), Abdulla, Alan Anwer, Multimedia Tools and Applications, ISSN 1573-7721, 2024.
Digital Object Identifier: 10.1007/s11042-024-20117-x
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[5] Rapid identification of tea quality by E-nose and computer vision combining with a synergetic data fusion strategy, Xu, Min, Wang, Jun, Gu, Shuang, Journal of Food Engineering, ISSN 0260-8774, Issue , 2019.
Digital Object Identifier: 10.1016/j.jfoodeng.2018.07.020
[CrossRef]

[6] Aroma quality characterization for Pixian broad bean paste fermentation by electronic nose combined with machine learning methods, Xu, Min, Wang, Xingbin, Xu, Zedong, Wang, Yao, Jia, Pengfei, ding, Wenwu, Dong, Shirong, Liu, Ping, Journal of Food Measurement and Characterization, ISSN 2193-4126, Issue 5, Volume 18, 2024.
Digital Object Identifier: 10.1007/s11694-024-02410-3
[CrossRef]

[7] Tea quality evaluation by applying E-nose combined with chemometrics methods, Xu, Min, Wang, Jun, Zhu, Luyi, Journal of Food Science and Technology, ISSN 0022-1155, Issue 4, Volume 58, 2021.
Digital Object Identifier: 10.1007/s13197-020-04667-0
[CrossRef]

[8] Parallel Hybrid Algorithm for Face Recognition Using Multi-Linear Methods, Alshiha, Abeer A. Mohamad, Al-Neama, Mohammed W., Qubaa, Abdalrahman R., International Journal of Electrical and Electronics Research, ISSN 2347-470X, Issue 4, Volume 11, 2023.
Digital Object Identifier: 10.37391/ijeer.110419
[CrossRef]

[9] Research on Teaching Characteristics of Innovative Civic and Political Education in Colleges and Universities Based on HOG Feature Extraction, Wei, Xuechun, Applied Mathematics and Nonlinear Sciences, ISSN 2444-8656, Issue 1, Volume 9, 2024.
Digital Object Identifier: 10.2478/amns-2024-0152
[CrossRef]

[10] Content Based Image Retrieval Method Based on SIFT Feature, He, Tao, Wei, Yong, Liu, Zhijun, Qing, Guorong, Zhang, Defen, 2018 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), ISBN 978-1-5386-4201-6, 2018.
Digital Object Identifier: 10.1109/ICITBS.2018.00169
[CrossRef]

[11] An Efficient method to Retrieve Diabetic Retinopathy Images using CBIR Technique, Suresh, Lakshmi, Chandran, Sreelekha, Vijayan, Divya, Vimina, E R, 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), ISBN 978-1-7281-4889-2, 2020.
Digital Object Identifier: 10.1109/ICCMC48092.2020.ICCMC-000195
[CrossRef]

[12] An Efficient and Lightweight Convolutional Neural Network for Carcinogenic Polyp Identification, Kayes, Md. Imrul, Prome, Rashida Feroz, Noor, Maria, Bhowmik, Shovan, Ahmed, Mamun, 2022 International Conference on Innovations in Science, Engineering and Technology (ICISET), ISBN 978-1-6654-8397-1, 2022.
Digital Object Identifier: 10.1109/ICISET54810.2022.9775824
[CrossRef]

[13] A Modified HOG Algorithm based on the Prewitt Operator, Li, Yu, Huang, Nanxi, Liu, Kongling, Chen, Hongguan, Wang, Ziwei, Yu, Juan, Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing, ISBN 9781450390002, 2021.
Digital Object Identifier: 10.1145/3448748.3448789
[CrossRef]

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