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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, email

Intelligent Automation & Soft Computing 2020, 26(4), 831-839. https://doi.org/10.32604/iasc.2020.010117

Abstract

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.

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APA Style
Feng, L., Li, H., Gao, Y., Zhang, Y. (2020). The application of sparse reconstruction algorithm for improving background dictionary in visual saliency detection. Intelligent Automation & Soft Computing, 26(4), 831-839. https://doi.org/10.32604/iasc.2020.010117
Vancouver Style
Feng L, Li H, Gao Y, Zhang Y. The application of sparse reconstruction algorithm for improving background dictionary in visual saliency detection. Intell Automat Soft Comput . 2020;26(4):831-839 https://doi.org/10.32604/iasc.2020.010117
IEEE Style
L. Feng, H. Li, Y. Gao, and Y. Zhang "The Application of Sparse Reconstruction Algorithm for Improving Background Dictionary in Visual Saliency Detection," Intell. Automat. Soft Comput. , vol. 26, no. 4, pp. 831-839. 2020. https://doi.org/10.32604/iasc.2020.010117



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