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Sparse metric-based mesh saliency.

Hu, Shanfeng and Liang, Xiaohui and Shum, Hubert P.H. and Li, Frederick W.B. and Aslam, Nauman (2020) 'Sparse metric-based mesh saliency.', Neurocomputing., 400 . pp. 11-23.

Abstract

In this paper, we propose an accurate and robust approach to salient region detection for 3D polygonal surface meshes. The salient regions of a mesh are those that geometrically stand out from their contexts and therefore are semantically important for geometry processing and shape analysis. However, a suitable definition of region contexts for saliency detection remains elusive in the field, and the previous methods fail to produce saliency maps that agree well with human annotations. We address these issues by computing saliency in a global manner and enforcing sparsity for more accurate saliency detection. Specifically, we represent the geometry of a mesh using a metric that globally encodes the shape distances between every pair of local regions. We then propose a sparsity-enforcing rarity optimization problem, solving which allows us to obtain a compact set of salient regions globally distinct from each other. We build a perceptually motivated 3D eye fixation dataset and use a large-scale Schelling saliency dataset for extensive benchmarking of saliency detection methods. The results show that our computed saliency maps are closer to the ground-truth. To showcase the usefulness of our saliency maps for geometry processing, we apply them to feature point localization and achieve higher accuracy compared to established feature detectors.

Item Type:Article
Full text:(AM) Accepted Manuscript
Available under License - Creative Commons Attribution Non-commercial No Derivatives.
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1016/j.neucom.2020.02.106
Publisher statement:© 2020 This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Date accepted:25 February 2020
Date deposited:13 March 2020
Date of first online publication:10 March 2020
Date first made open access:10 March 2021

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