Open AccessLocal image descriptor based on spectral embedding
Author(s) -
Yan Pu,
Tang Jun,
Zhu Ming,
Liang Dong
Publication year - 2015
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.2014.0124
Subject(s) - artificial intelligence , computer vision , embedding , pattern recognition (psychology) , curvature , mathematics , computer science , invariant (physics) , geometry , mathematical physics
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.