Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)

Paper-cutting Image Retrieval Technology based on LSH Improvement

Authors
Xintong Liu, Huaxiong Zhang
Corresponding Author
Xintong Liu
Available Online April 2019.
DOI
https://doi.org/10.2991/icmeit-19.2019.37How to use a DOI?
Keywords
paper-cutting image retrieval; OTSU algorithm; LBP textural features; LSH index.
Abstract
Characteristics of paper-cutting, paper-cutting gallery construction, etc. are analyzed aiming at existing problems of folk traditional paper-cutting art multimedia interactive platform. It is proposed that rotation invariant LBP (Local Binary Pattern) is combined with LSH algorithm to present a large-scale paper-cutting image fast retrieval method. Firstly, the background image is eliminated with OTSU, then the paper-cutting image rotation LBP feature. In addition, high-dimensional data is mapped to low dimensional space, and a hash index was constructed by local sensitive hash algorithm to find the approximate KNN. Experimental result in the dataset shows that the improved algorithm has high accuracy on paper-cutting image retrieval, and it is significantly higher than traditional algorithm on retrieval speed.
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Proceedings
3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)
Part of series
Advances in Computer Science Research
Publication Date
April 2019
ISBN
978-94-6252-708-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/icmeit-19.2019.37How 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  - Xintong Liu
AU  - Huaxiong Zhang
PY  - 2019/04
DA  - 2019/04
TI  - Paper-cutting Image Retrieval Technology based on LSH Improvement
BT  - 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-19.2019.37
DO  - https://doi.org/10.2991/icmeit-19.2019.37
ID  - Liu2019/04
ER  -