We have located links that may give you full text access.
Saliency Transfer: A Correspondence Based Approach for Saliency Detection.
IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society 2016 August 20
In this paper, we show that large annotated datasets datasets have great potential to provide strong priors for saliency estimation rather than merely serving for benchmark evaluations. To this end, we present a novel image saliency detection method called saliency transfer. Given an input image, we first retrieve a support set of best matches from the large database of saliency annotated images. Then, we assign the transitional saliency scores by warping the support set annotations onto the input image according to computed dense correspondences. To incorporate context, we employ two complementary correspondence strategies: a global matching scheme based on scene-level analysis, and a local matching scheme based on patch-level inference. We then introduce two refinement measures to further refine the saliency maps, and apply the random-walk-with-restart by exploring the global saliency structure to estimate the affinity between foreground and background assignments. Extensive experimental results on four publicly available benchmark datasets demonstrate that the proposed saliency algorithm consistently outperforms the current state-of-the-art methods.
Full text links
Related Resources
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app