# Non-convex non-local flows for saliency detection

Source: [Non-convex non-local flows for saliency detection](<https://www.sota2.com/research/paper/68fa54d61e209131ce98f09d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-06-05T05:01:36.957Z

Authors: Iv\\'an Ram\\'irez, Gonzalo Galiano, Emanuele Schiavi

Published: 2018-05-23T20:03:06.000Z

## Abstract

We propose and numerically solve a new variational model for automatic saliency detection in digital images. Using a non-local framework we consider a family of edge preserving functions combined with a new quadratic saliency detection term. Such term defines a constrained bilateral obstacle problem for image classification driven by p-Laplacian operators, including the so-called hyper-Laplacian case (0 \< p \< 1). The related non-convex non-local reactive flows are then considered and applied for glioblastoma segmentation in magnetic resonance fluid-attenuated inversion recovery (MRI-Flair) images. A fast convolutional kernel based approximated solution is computed. The numerical experiments show how the non-convexity related to the hyperLaplacian operators provides monotonically better results in terms of the standard metrics.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1805.09408>)

[PDF](<https://arxiv.org/pdf/1805.09408.pdf>)