Light Field Salient Object Detection via Hybrid Priors

摘要

In this paper, we propose a salient object detection model on light field via hybird priors. The proposed model extracts four feature maps, including region contrast, background prior, depth prior and surface orientation prior maps. After that, the priors fusion stage is implemented to obtain and optimize the final salient object map. To verify the validity of the proposed model, comprehensive performance evaluation and comparative analysis are conducted on the public datasets LFSD and HFUT-Lytro. Experimental results show that the proposed method is superior to the existing light field saliency object detection model on the public two datasets.