Application of SPCA Algorithm in Image Dimensionality Reduction
XianWei Wu, WenYang Yu, YuBin Yang
Available Online March 2014.
- https://doi.org/10.2991/mce-14.2014.126How to use a DOI?
- Dimensionality Reduction;SPCA;PCA;GHA;Image Feature
- 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.
- 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 -