Salient Object Detection Using Semantic Prior Analysis PROJECT TITLE : Semantic Prior Analysis for Salient Object Detection ABSTRACT: 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. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Recurrent Wavelet Learning with Visibility Enhancement for Scale-Free Single Image Deraining For Visual Maritime Surveillance, Single Image Defogging Based on Illumination Decomposition
