Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications

Robustness Evaluation of Local Descriptors

Authors
Binquan Li, Xiaohui Hu
Corresponding Author
Binquan Li
Available Online January 2016.
DOI
https://doi.org/10.2991/icaita-16.2016.18How to use a DOI?
Keywords
local descriptor; robust; extracting features; image changing
Abstract
The descriptors should be robust to changes of images taken under various conditions in order to obtain correct recognition. In this paper, we compare the robustness of descriptors computed for local interest regions, for example, extracted by the Harris, SURF and MSER detector. Different descriptors have been compared, depending on the interest region detector. Our evaluation uses different methods of matching features to assess image transformations. We compare SIFT, SURF, SURF with regions detected by MSER, FREAK for interest regions. The results show that local descriptors are sensitive to the viewpoint change, zoom and rotation, and image blur. Illumination has less effect on the robustness of image changing. SURF with regions detected by MSER performs better and is recommended for its better robustness.
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This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications
Series
Advances in Intelligent Systems Research
Publication Date
January 2016
ISBN
978-94-6252-162-9
ISSN
1951-6851
DOI
https://doi.org/10.2991/icaita-16.2016.18How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Binquan Li
AU  - Xiaohui Hu
PY  - 2016/01
DA  - 2016/01
TI  - Robustness Evaluation of Local Descriptors
BT  - Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications
PB  - Atlantis Press
SP  - 70
EP  - 73
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaita-16.2016.18
DO  - https://doi.org/10.2991/icaita-16.2016.18
ID  - Li2016/01
ER  -