Application of SPCA Algorithm in Image Dimensionality Reduction

DOI

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.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

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  - Proceedings of the 2014 International Conference on Mechatronics, Control and Electronic Engineering
PB  - Atlantis Press
SP  - 565
EP  - 570
SN  - 1951-6851
UR  - https://doi.org/10.2991/mce-14.2014.126
DO  - 10.2991/mce-14.2014.126
ID  - Wu2014/03
ER  -