Semantic Prior Analysis for Salient Object Detection


The goal of salient item recognition is to identify the image's most important elements. Semantic priors are integrated into the salient object recognition procedure in this paper. The method begins by obtaining an explicit saliency map, which is refined by the explicit semantic priors learned from the data.. A trained model is used to create an implicit saliency map, which maps the saliency values to the implicit semantic priors inherent in superpixel features. When both explicit and implicit semantic maps are fused together, an adaptive saliency map is created. Ultimately, the final saliency map is produced in the post-processing refinement step. ECSSD, HKUIS, and iCoSeg were used to demonstrate how well the proposed technique performs compared to the state of the art baselines on these three tough datasets.

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