CLC number: TP391.41
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2013-06-06
Cited: 9
Clicked: 6805
Jian Cao, Dian-hui Mao, Qiang Cai, Hai-sheng Li, Jun-ping Du. A review of object representation based on local features[J]. Journal of Zhejiang University Science C, 2013, 14(7): 495-504.
@article{title="A review of object representation based on local features",
author="Jian Cao, Dian-hui Mao, Qiang Cai, Hai-sheng Li, Jun-ping Du",
journal="Journal of Zhejiang University Science C",
volume="14",
number="7",
pages="495-504",
year="2013",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.CIDE1303"
}
%0 Journal Article
%T A review of object representation based on local features
%A Jian Cao
%A Dian-hui Mao
%A Qiang Cai
%A Hai-sheng Li
%A Jun-ping Du
%J Journal of Zhejiang University SCIENCE C
%V 14
%N 7
%P 495-504
%@ 1869-1951
%D 2013
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.CIDE1303
TY - JOUR
T1 - A review of object representation based on local features
A1 - Jian Cao
A1 - Dian-hui Mao
A1 - Qiang Cai
A1 - Hai-sheng Li
A1 - Jun-ping Du
J0 - Journal of Zhejiang University Science C
VL - 14
IS - 7
SP - 495
EP - 504
%@ 1869-1951
Y1 - 2013
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.CIDE1303
Abstract: Object representation based on local features is a topical subject in the domain of image understanding and computer vision. We discuss the defects of global features in present methods and the advantages of local features in object recognition, and briefly explore state-of-the-art recognition methods using local features, especially the main approaches of local feature extraction and object representation. To clearly explain these methods, the problem of local feature extraction is divided into feature region detection, feature region description, and feature space optimization. The main components and merits of these steps are presented. Technologies for object presentation are classified into three types: vector space, sliding window, and structure relationship models. Future development trends are discussed briefly.
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