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The Application of Sparse Reconstruction Algorithm for Improving Background Dictionary in Visual Saliency Detection

Lei Feng1,2, Haibin Li1,*, Yakun Gao1, Yakun Zhang1

1 Yanshan University, School of Electrical Engineering, Qinhuangdao, China;
2 Xingtai Polytechnic College, China
Full Mailing Address: No. 438, west section of Hebei Street, Qinhuangdao City, Hebei Province, China

* Corresponding Author: Haibin Li,

Intelligent Automation & Soft Computing 2020, 26(4), 831-839.


In the paper, we apply the sparse reconstruction algorithm of improved background dictionary to saliency detection. Firstly, after super-pixel segmentation, two bottom features are extracted: the color information of LAB and the texture features of the image by Gabor filter. Secondly, the convex hull theory is used to remove object region in boundary region, and K-means clustering algorithm is used to continue to simplify the background dictionary. Finally, the saliency map is obtained by calculating the reconstruction error. Compared with the mainstream algorithms, the accuracy and efficiency of this algorithm are better than those of other algorithms.


Cite This Article

L. Feng, H. Li, Y. Gao and Y. Zhang, "The application of sparse reconstruction algorithm for improving background dictionary in visual saliency detection," Intelligent Automation & Soft Computing, vol. 26, no.4, pp. 831–839, 2020.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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