The goal of salient object detection is to estimate the regions which are most likely to attract human’s
visual attention. As an important image preprocessing procedure to reduce the computational complexity, salient
object detection is still a challenging problem in computer vision. In this paper, we proposed a salient object
detection model by integrating local and global superpixel contrast at multiple scales. Three features are computed
to estimate the saliency of superpixel. Two optimization measures are utilized to refine the resulting saliency
map. Extensive experiments with the state-of-the-art saliency models on four public datasets demonstrate the
effectiveness of the proposed model.
MU Nan1 (穆楠), XU Xin1,2* (徐 新), WANG Yinglin3 (王英林), ZHANG Xiaolong1,2 (张晓龙)
. A Multiscale Superpixel-Level Salient Object Detection Model Using Local-Global Contrast Cue[J]. Journal of Shanghai Jiaotong University(Science), 2017
, 22(1)
: 121
-128
.
DOI: 10.1007/s12204-017-1810-z
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