Semantic prior analysis for salient object detection

Tam V. Nguyen, Khanh Nguyen, Thanh-Toan Do

Research output: Contribution to journalArticleResearchpeer-review

12 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)3130-3141
Number of pages12
JournalIEEE Transactions on Image Processing
Volume28
Issue number6
DOIs
Publication statusPublished - Jun 2019
Externally publishedYes

Keywords

  • deep networks
  • Salient object detection
  • semantic priors

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