Erratum to “Hierarchizing graph-based image segmentation algorithms relying on region dissimilarity: the case of the Felzenszwalb-Huttenlocher method”

Int. J. Patt. Recogn. Artif. Intell.

Kenmochi³

et al. 2019

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Hierarchical image segmentation provides a region-oriented scale-space, i.e. a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. However, most image segmentation algorithms, among which a graph-based image segmentation method relying on a region merging criterion was proposed by Felzenszwalb–Huttenlocher in 2004, do not lead to a hierarchy. In order to cope with a demand for hierarchical segmentation, Guimarães et al. proposed in 2012 a method for hierarchizing the popular Felzenszwalb–Huttenlocher method, without providing an algorithm to compute the proposed hierarchy. This paper is devoted to providing a series of algorithms to compute the result of this hierarchical graph-based image segmentation method efficiently, based mainly on two ideas: optimal dissimilarity measuring and incremental update of the hierarchical structure. Experiments show that, for an image of size 321 × 481 pixels, the most efficient algorithm produces the result in half a second whereas the most naive one requires more than 4 h.

Section: Hierarchical Graph-based Segmentation Methodsmentioning

confidence: 99%

Section: Methods 1 Hgb Methodsmentioning

confidence: 99%

“…13 However, the later method allows us to¯nd maximal not-too-coarse hierarchies (see Ref. 13 for more details).…”

Section: Methods 1 Hgb Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

See 3 more Smart Citations

Efficient Algorithms for Hierarchical Graph-Based Segmentation Relying on the Felzenszwalb–Huttenlocher Dissimilarity

Cayllahua-Cahuina

Int. J. Patt. Recogn. Artif. Intell.

Kenmochi³

et al. 2019

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A Study of Observation Scales Based on Felzenswalb-Huttenlocher Dissimilarity Measure for Hierarchical Segmentation

Cayllahua-Cahuina

Discrete Geometry for Computer Imagery

Guimarães

et al. 2019

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Hierarchical image segmentation provides a region-oriented scale-space, i.e., a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. Guimarães et al. proposed a hierarchical graph based image segmentation (HGB) method based on the Felzenszwalb-Huttenlocher dissimilarity. This HGB method computes, for each edge of a graph, the minimum scale in a hierarchy at which two regions linked by this edge should merge according to the dissimilarity. In order to generalize this method, we first propose an algorithm to compute the intervals which contain all the observation scales at which the associated regions should merge. Then, following the current trend in mathematical morphology to study criteria which are not increasing on a hierarchy, we present various strategies to select a significant observation scale in these intervals. We use the BSDS dataset to assess our observation scale selection methods. The experiments show that some of these strategies lead to better segmentation results than the ones obtained with the original HGB method.

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

Lecture Notes in Computer Science

Phelippeau²,

Carneiro

et al. 2021

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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.