Local image descriptor based on spectral embedding

被引:2
作者
Yan, Pu [1 ]
Tang, Jun [1 ]
Zhu, Ming [1 ]
Liang, Dong [1 ]
机构
[1] Anhui Univ, Sch Elect & Informat Engn, Hefei 230601, Peoples R China
基金
中国国家自然科学基金; 高等学校博士学科点专项科研基金;
关键词
CLASSIFICATION; SEGMENTATION; CATEGORIES; RETRIEVAL; TEXTURE; PATTERN; POINTS; SCALE;
D O I
10.1049/iet-cvi.2014.0124
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This study presents a local image descriptor based on spectral embedding. Specifically, the spectra of line graph are used to represent image edges, corners and edge points with big curvature. The authors theoretically analyse and experimentally verify that the spectra of line graph are robust to noise and are invariant to rotation and linear intensity changes. Based on such a fact, some local image descriptors are constructed using the spectra of line graph. Comparative experiments demonstrate the effectiveness of the proposed descriptor and its superiority to some state-of-the-art descriptors under image rotation, image blur, viewpoint change, illumination change, JPEG compression and noise.
引用
收藏
页码:278 / 289
页数:12
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