Abstract
Learning-based partial differential equations (PDEs), which combine fundamental differential invariants into a nonlinear regressor, have been successfully applied to several computer vision and image processing problems. However, it cannot apply to saliency detection directly. In this paper, we present a novel learning-based PDEs model and learn the PDEs from training samples. We simplify the current model by setting the indicate function constant along with the evolution process. When learned the PDEs, we first combine three simple priors for the pre-processing and then solve the PDEs to generate the final saliency map for each image. Experimental results on public benchmark data set MSRA-1000 demonstrate the superiority of our hybrid approach.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
