TY - GEN
T1 - Sparsity-motivated multi-scale histograms of oriented gradients feature for SRC
AU - Zhang, Suoqi
AU - Gong, Jiulu
AU - Chen, Derong
AU - Xu, Linfeng
AU - Yan, Lei
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - In order to recognize targets accurately from the low-quality images obtained from unmanned system, sparse representation based classification (SRC) method using sparsity-motivated gradient feature was proposed. The multi-scale histograms of oriented gradients (HOG) feature was used as an original feature, whose dimension was reduced by a non-adaptive random projection method. A very sparse measurement matrix was adopted to preserve the structure of multi-scale HOG feature space efficiently. The sparse representation was obtained via i1-norm minimization, and the least reconstruction error was used as recognition principle. Experiment results again Comanche FLIR data set show that, the proposed method can raise the recognition rate by 2% compared with the state of art methods.
AB - In order to recognize targets accurately from the low-quality images obtained from unmanned system, sparse representation based classification (SRC) method using sparsity-motivated gradient feature was proposed. The multi-scale histograms of oriented gradients (HOG) feature was used as an original feature, whose dimension was reduced by a non-adaptive random projection method. A very sparse measurement matrix was adopted to preserve the structure of multi-scale HOG feature space efficiently. The sparse representation was obtained via i1-norm minimization, and the least reconstruction error was used as recognition principle. Experiment results again Comanche FLIR data set show that, the proposed method can raise the recognition rate by 2% compared with the state of art methods.
KW - automatic target recognition
KW - compressive sensing
KW - histograms of oriented gradients
KW - sparse representation
UR - https://www.scopus.com/pages/publications/85050883512
U2 - 10.1109/ICUS.2017.8278375
DO - 10.1109/ICUS.2017.8278375
M3 - Conference contribution
AN - SCOPUS:85050883512
T3 - Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017
SP - 389
EP - 393
BT - Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017
A2 - Xu, Xin
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Unmanned Systems, ICUS 2017
Y2 - 27 October 2017 through 29 October 2017
ER -