Open AccessEfficient rotation‐invariant histogram of oriented gradient descriptors for car detection in satellite images
Author(s) -
Su Ang,
Sun Xiaoliang,
Zhang Yueqiang,
Yu Qifeng
Publication year - 2016
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2015.0333
Subject(s) - histogram , artificial intelligence , histogram of oriented gradients , computer science , invariant (physics) , computer vision , pattern recognition (psychology) , fourier transform , computation , rotation (mathematics) , feature extraction , curse of dimensionality , mathematics , algorithm , image (mathematics) , mathematical analysis , mathematical physics
Cars can appear at any orientations in satellite images, but the widely used traditional histogram of oriented gradient (HOG) features is not rotation‐invariant. Recently, a rotation‐invariant HOG descriptor using Fourier analysis in polar coordinators has been proposed and has shown good performance; however, it is time and memory consuming. In this study, the authors improve this method to present an efficient rotation‐invariant HOG descriptor for car detection in satellite images. The authors first convert spatial convolutions to multiplications in the frequency domain based on fast Fourier transform to speed up the descriptor computation. Then, they employ a backward search feature selection method based on support vector machine to reduce the descriptor dimensionality. This allows them to build the proposed efficient rotation‐invariant HOG descriptor at low time and memory cost. The authors demonstrate that their method is more efficient and yields better detection performance in a public dataset for car detection task in satellite images.