Proceedings of the 2014 International Conference on Mechatronics, Control and Electronic Engineering

Application of SPCA Algorithm in Image Dimensionality Reduction

Authors
XianWei Wu, WenYang Yu, YuBin Yang
Corresponding Author
XianWei Wu
Available Online March 2014.
DOI
https://doi.org/10.2991/mce-14.2014.126How to use a DOI?
Keywords
Dimensionality Reduction;SPCA;PCA;GHA;Image Feature
Abstract
In the page, We discuss several dimensionality reduction methods for image feature, and then focus on the one: SPCA(Simple Primary Component Analysis), which is simple fast and exceeding algorithm of data-oriented PCA algorithm. In order to better understand the SPCA algo-rithm, Some well-designed experiments of image compres-sion and image retrieval are taken to compare these algo-rithms. By experiment 1, we get the result: PCA matrix algorithm is best in performance but worst in speed, and GHA is better in speed ,but worst in performance, and the results show that SPCA is out-standing not only in perfor-mance, but also in speed. By experiment 2, we get the desired result: using the image feature after SPCA almost get the same performance of original image feature, but much better than original image feature in speed. The conclusion is: SPCA algorithm can be applied in many field, especially in image compression and image retrieval.
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Proceedings
2014 International Conference on Mechatronics, Control and Electronic Engineering (MCE-14)
Part of series
Advances in Intelligent Systems Research
Publication Date
March 2014
ISBN
978-94-62520-31-8
ISSN
1951-6851
DOI
https://doi.org/10.2991/mce-14.2014.126How 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  - XianWei Wu
AU  - WenYang Yu
AU  - YuBin Yang
PY  - 2014/03
DA  - 2014/03
TI  - Application of SPCA Algorithm in Image Dimensionality Reduction
BT  - 2014 International Conference on Mechatronics, Control and Electronic Engineering (MCE-14)
PB  - Atlantis Press
SP  - 565
EP  - 570
SN  - 1951-6851
UR  - https://doi.org/10.2991/mce-14.2014.126
DO  - https://doi.org/10.2991/mce-14.2014.126
ID  - Wu2014/03
ER  -