An efficacious medical video segmentation approach with a moving camera

Zou, Xiaochun; Zhao, Xinbo; Chi, Zheru

doi:10.1109/icch.2012.6724463

Cited by 1 publication

(1 citation statement)

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“…The computational efficiency of these simple algorithms is paramount in large-scale machine learning problems [14]- [16], e.g., those with high-resolution video data. Second, safety-critical video segmentation applications, such as autonomous driving [17] and medical imaging [18], demand global optimality guarantees to promise consistent performance and safety margins. With the recent influx of studies on spurious local minima of nonconvex optimization problems [19]- [21], we approach this problem by exploiting new results on the benign landscape of rank-1 NRPCA [9].…”

Section: Introductionmentioning

confidence: 99%

Global Optimality Guarantees for Nonconvex Unsupervised Video Segmentation

Anderson

Sojoudi

2019

2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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In this paper, we consider the problem of unsupervised video object segmentation via background subtraction. Specifically, we pose the nonsemantic extraction of a video's moving objects as a nonconvex optimization problem via a sum of sparse and low-rank matrices. The resulting formulation, a nonnegative variant of robust principal component analysis, is more computationally tractable than its commonly employed convex relaxation, although not generally solvable to global optimality. In spite of this limitation, we derive intuitive and interpretable conditions on the video data under which the uniqueness and global optimality of the object segmentation are guaranteed using local search methods. We illustrate these novel optimality criteria through example segmentations using real video data.

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