Image Compared by Election Campaign Algorithm

DOI

10.2991/meici-16.2016.200How to use a DOI?

Keywords

Image comparison; Content-based image retrieval; Grey scale feature; Optimization; Election campaign algorithm

Abstract

Usually CBIR (Content-based image retrieval) is an image retrieval method that exploits the feature of the image as the retrieval index, which is based upon the content, including colors, textures, shapes and distributions of objects in the image. After the feature detecting, the composition of the similarity matching image set is found, then detecting the most matching image still need to be process in the higher level analysis and retrieval. It is a difficult and slow process. So, if we take an opposite approach, detecting the not-match image from the similarity matching image set but comparing all the images in the set, it can be more easily to achieve. In this paper, we propose an new image comparison method base on Election Campaign Algorithm, which provide parallel and fast optimum feature detecting, to detect the not-match images from the similarity matching image set, then another method would be use to find the most-match images. With this method, the image comparison process is fast, the size-reduce image set is quickly to be received.

Copyright

© 2016, 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  - Qinghua Xie
AU  - Xiangwei Zhang
AU  - Wenge Lv
AU  - Siyuan Cheng
PY  - 2016/09
DA  - 2016/09
TI  - Image Compared by Election Campaign Algorithm
BT  - Proceedings of the 2016 6th International Conference on Management, Education, Information and Control (MEICI 2016)
PB  - Atlantis Press
SP  - 962
EP  - 967
SN  - 1951-6851
UR  - https://doi.org/10.2991/meici-16.2016.200
DO  - 10.2991/meici-16.2016.200
ID  - Xie2016/09
ER  -