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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
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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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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: 218 | Views: 374

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  «-- Click to see who has cited this paper

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[CrossRef] [SCOPUS Times Cited 3]


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[CrossRef] [SCOPUS Times Cited 7]


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[CrossRef]


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[CrossRef] [SCOPUS Times Cited 8]


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[CrossRef] [Web of Science Times Cited 377]


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[CrossRef] [Web of Science Times Cited 19] [SCOPUS Times Cited 23]


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[CrossRef] [SCOPUS Times Cited 21]






References Weight

Web of Science® Citations for all references: 1,585 TCR
SCOPUS® Citations for all references: 2,170 TCR

Web of Science® Average Citations per reference: 53 ACR
SCOPUS® Average Citations per reference: 72 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2017-06-26 18:56 in 178 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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