MATEC Web Conf.
Volume 176, 20182018 6th International Forum on Industrial Design (IFID 2018)
|Number of page(s)||4|
|Section||Information Technology and Cloud Design Service Platform|
|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: email@example.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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