| Issue |
MATEC Web Conf.
Volume 176, 2018
2018 6th International Forum on Industrial Design (IFID 2018)
|
|
|---|---|---|
| Article Number | 03009 | |
| Number of page(s) | 4 | |
| Section | Information Technology and Cloud Design Service Platform | |
| DOI | https://doi.org/10.1051/matecconf/201817603009 | |
| Published online | 02 July 2018 | |
Saliency Detection with Sparse Prototypes: An Approach Based on Multi-Dictionary Sparse Encoding
College of Communications Engineering, Army Engineering University of PLA, Nanjing, China.
* Corresponding author: wangjun_ice@126.com
This paper proposes a bottom-up saliency detection algorithm based on multi-dictionary sparse recovery. Firstly, the SLIC algorithm is used to segment the image into superpixels in multilevel and atoms with a high background possibility are selected from the boundary superpixels to construct the multidictionary. Secondly, sparse recovery of the entire image is achieved using multi-dictionary to get subsaliency maps from the perspective of sparse recovery errors. The final saliency map is generated in a weighted fusion manner. Experimental results on three public datasets demonstrate the effectiveness of our model.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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