Lei Feng1,2, Haibin Li1,*, Yakun Gao1, Yakun Zhang1
Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 831-839, 2020, DOI: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. More >
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