Semantic Prior Analysis for Salient Object Detection
Document Type
Article
Publication Date
1-23-2019
Publication Source
IEEE Transactions on Image Processing
Abstract
Salient object detection aims to detect the main objects in the given image. In this paper, we propose an approach that integrates semantic priors into the salient object detection process. The method first obtains an explicit saliency map that is refined by the explicit semantic priors learned from data. Then an implicit saliency map is constructed using a trained model that maps the implicit semantic priors embedded into superpixel features with the saliency values. Next, the fusion saliency map is computed by adaptively fusing both the explicit and implicit semantic maps. The final saliency map is eventually computed via the post-processing refinement step. Experimental results have demonstrated the effectiveness of the proposed method; particularly, it achieves competitive performance with the state-of-the-art baselines on three challenging datasets, namely, ECSSD, HKUIS, and iCoSeg.
Inclusive pages
3130 - 3141
ISBN/ISSN
1057-7149
Publisher
IEEE
Volume
28
Issue
6
Keywords
Salient object detection, semantic priors, deep networks
eCommons Citation
Nguyen, Tam V.; Nguyen, Khanh; and Do, Thanh-Toan, "Semantic Prior Analysis for Salient Object Detection" (2019). Computer Science Faculty Publications. 222.
https://ecommons.udayton.edu/cps_fac_pub/222
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