An Improved Region Contrast and Global Distribution Saliency Detection algorithm

DOI

10.2991/isaeece-16.2016.65How to use a DOI?

Keywords

Saliency detection, region contrast, global distribution

Abstract

According to the local contrast and global distribution of an image, this paper detecting salient images through bottom-up data driven . First, this paper using adaptive segmentation method divided image into non-overlapping images, improved Block and Chessboar distance from a linear combination to replace the Euclidean distance method to calculate the regional features of contrast functions, then calculate the global distribution of feature functions, finally fusion of the above features for computing saliency map. The algorithm taking into account local features and global features to get more accurate saliency map. Test our method on the international public data sets MSRA-1000,the experimental result proves that the images extracted by this method are more accurate and more clearly, while reducing the calculation time of regional characteristics, having strong noise and high texture regions resistance , and can basically ignore the complex background.

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/).

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TY  - CONF
AU  - Xiaohong Hao
AU  - Yifang Yuan
AU  - Wanfei Jiang
PY  - 2016/04
DA  - 2016/04
TI  - An Improved Region Contrast and Global Distribution Saliency Detection algorithm
BT  - Proceedings of the 2016 International Symposium on Advances in Electrical, Electronics and Computer Engineering
PB  - Atlantis Press
SP  - 338
EP  - 341
SN  - 2352-5401
UR  - https://doi.org/10.2991/isaeece-16.2016.65
DO  - 10.2991/isaeece-16.2016.65
ID  - Hao2016/04
ER  -