On minimum spanning tree streaming for hierarchical segmentation

Gigli, Leonardo; Velasco-Forero, Santiago; Marcotegui, Beatriz

doi:10.1016/j.patrec.2020.07.006

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…Collectively they enable the handling of texture-rich regions that cannot be clustered into meaningful segments, and compute the unsupervised segmentation of images by seeking for extreme attribute values. Gigli et al (2020) investigate the construction of the minimum spanning tree in streaming for images. They focus on the problem of computing a minimum spanning tree of the union of two graphs with a non-empty intersection.…”

Section: Contributionsmentioning

confidence: 99%

Editorial — Virtual Special Issue: “Hierarchical Representations: New Results and Challenges for Image Analysis”

Passat

Kurtz

Vacavant

2020

Pattern Recognition Letters

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Section: Contributionsmentioning

confidence: 99%

Editorial — Virtual Special Issue: “Hierarchical Representations: New Results and Challenges for Image Analysis”

Passat

Kurtz

Vacavant

2020

Pattern Recognition Letters

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“…In [4,6,8], the authors investigate distributed memory algorithms to compute min and max trees for terabytes images. In [5], computation of minimum spanning trees of streaming images is considered. A parallel algorithm for the computation of quasi-flat zones hierarchies has been proposed in [7].…”

Section: Introductionmentioning

confidence: 99%

Join, Select, and Insert: Efficient Out-of-core Algorithms for Hierarchical Segmentation Trees

Lefèvre

Cousty

Phelippeau³

2022

Lecture Notes in Computer Science

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Binary Partition Hierarchies (BPH) and minimum spanning trees are fundamental data structures involved in hierarchical analysis such as quasi-flat zones or watershed. However, classical BPH construction algorithms require to have the whole data in memory, which prevent the processing of large images that cannot fit entirely in the main memory of the computer. To cope with this problem, an algebraic framework leading to a high level calculus was introduced allowing an out-of-core computation of BPHs. This calculus relies on three operations: select, join, and insert. In this article, we introduce three efficient algorithms to perform these operations providing pseudo-code and complexity analysis.

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“…Indeed, giga-or tera-bytes images, which become common with the improvements in sensor resolution, cannot fit within the main memory of a computer and recent (deep) learning methods require browsing several times datasets of millions of images. The classical sequential hierarchy algorithms are not adapted to these situations and therefore need to be completely redesigned [10,5,7,6] to cope with these situations.…”

Section: Introductionmentioning

confidence: 99%

“…In [10,5,7], the authors investigate parallel algorithms to compute the min and max trees -hierarchical structures used for various applications, such as attribute filtering and segmentation -of terabytes images. In [6], the computation of minimum spanning trees of streaming images is considered. In [9], the authors investigate the parallel computation of quasi-flat zones hierarchies.…”

Section: Introductionmentioning

confidence: 99%

An Algebraic Framework for Out-of-Core Hierarchical Segmentation Algorithms

Cousty

Phelippeau²,

Carneiro

et al. 2021

Lecture Notes in Computer Science

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Binary partition hierarchies and minimum spanning trees are key structures for numerous hierarchical analysis methods, as those involved in computer vision and mathematical morphology. In this article, we consider the problem of their computation in an out-of-core manner, i.e., by minimizing the size of the data structures that are simultaneously needed at the different computation steps. Out-of-core algorithms are necessary when the data are too large to fit entirely in the main memory of the computer, which can be the case with very large images in 2-, 3-, or higher dimension space. We propose a new algebraic framework composed of four main operations on hierarchies: edge-addition, select, insert, and join. Based on this framework, we propose and establish the correctness of an out-of-core calculus for binary partition hierarchies and for minimum spanning trees. First applications to image processing suggest the practical efficiency of this calculus.

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On minimum spanning tree streaming for hierarchical segmentation

Cited by 4 publications

References 22 publications

Editorial — Virtual Special Issue: “Hierarchical Representations: New Results and Challenges for Image Analysis”

Editorial — Virtual Special Issue: “Hierarchical Representations: New Results and Challenges for Image Analysis”

Join, Select, and Insert: Efficient Out-of-core Algorithms for Hierarchical Segmentation Trees

An Algebraic Framework for Out-of-Core Hierarchical Segmentation Algorithms

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