Self-organization in vision: stochastic clustering for image segmentation, perceptual grouping, and image database organization

Gdalyahu, Yoram; Weinshall, Daphna; Werman, Michael

doi:10.1109/34.954598

Cited by 145 publications

(96 citation statements)

References 33 publications

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“…Thus performing MAP inference under this probability distribution will still lead to trivial segmentations. However, as pointed out by [1,5], there is far more information in the full probability distribution over partitions than solely in the MAP partition. For example, consider the pairwise correlation Ô´ µ defined for any two neighboring nodes in the graph as the probability that they belong to the same segment:…”

Section: Typical Cutsmentioning

confidence: 99%

“…The criterion was first introduced by Blatt et al [1] in the framework of statistical physics, and was reformulated by Gdalyahu et al [5] in terms of graph partitioning and image segmentation. Unlike most graph partitioning algorithms, this one is directly based on a probability distribution over partitions.…”

Section: Typical Cutsmentioning

confidence: 99%

“…While efficient approximate algorithms for normalized cut are available through eigensolvers, to this day, there has been no similar algorithm for typical cuts. Both [1,5] used sophisticated stochastic algorithms whose behavior for finite samples is difficult to analyze. In fact, the problem of determining the equilibrium distribution over segmentations from which the algorithm used in [5] samples from, is an open one.…”

Section: Typical Cutsmentioning

confidence: 99%

See 2 more Smart Citations

Learning and inferring image segmentations using the GBP typical cut algorithm

Shental

Zomet

Hertz

et al. 2003

Proceedings Ninth IEEE International Conference on Computer Vision

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show abstract

Section: Typical Cutsmentioning

confidence: 99%

Section: Typical Cutsmentioning

confidence: 99%

Section: Typical Cutsmentioning

confidence: 99%

See 1 more Smart Citation

Learning and inferring image segmentations using the GBP typical cut algorithm

Shental

Zomet

Hertz

et al. 2003

Proceedings Ninth IEEE International Conference on Computer Vision

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show abstract

“…Many discussions on what is or how to get natural segmentation results have been published [16] and a significant effort has been devoted to developing complex scene image segmentation [17,18], perceptual grouping of image contents [19,20] or perceptually based colour texture analysis [21]. A lot of work in this direction has also been based on perceptual colour spaces [22].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised colour image segmentation by low-level perceptual grouping

Martínez-Usó

Pla

García-Sevilla

2011

Pattern Anal Applic

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This paper proposes a new unsupervised approach for colour image segmentation. A hierarchy of image partitions is created on the basis of a function that merges spatially connected regions according to primary perceptual criteria. Likewise, a global function that measures the goodness of each defined partition is used to choose the best low-level perceptual grouping in the hierarchy. Contributions also include a comparative study with five unsupervised colour image segmentation techniques. These techniques have been frequently used as a reference in other comparisons. The results obtained by each method have been systematically evaluated using four well-known unsupervised measures for judging the segmentation quality. Our methodology has globally shown the best performance, obtaining better results in three out of four of these segmentation quality measures. Experiments will also show that our proposal finds low-level perceptual solutions that are highly correlated with the ones provided by humans.

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“…This process is often called perceptual grouping, a concept from Gestalt psychology [1], which has also been used in the field of computer vision before [2][3][4]. Filters based on visual cortex models, and in particular grouping of features have already been developed [5,6].…”

Section: Introductionmentioning

confidence: 99%

Attribute-space connectivity and connected filters

Wilkinson

2007

Image and Vision Computing

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In this paper connected operators from mathematical morphology are extended to a wider class of operators, which are based on connectivities in higher dimensional spaces, similar to scale spaces, which will be called attribute-spaces. Though some properties of connected filters are lost, granulometries can be defined under certain conditions, and pattern spectra in most cases. The advantage of this approach is that regions can be split into constituent parts before filtering more naturally than by using partitioning connectivities. Furthermore, the approach allows dealing with overlap, which is impossible in connectivity. A theoretical comparison to hyperconnectivity suggests the new concept is different. The theoretical results are illustrated by several examples. These show how attribute-space connected filters merge the ability of filtering based on local structure using classical, structuring-element-based filters to the object-attribute-based filtering of connected filters, and how this differs from similar attempts using second-generation connectivity.

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Self-organization in vision: stochastic clustering for image segmentation, perceptual grouping, and image database organization

Cited by 145 publications

References 33 publications

Learning and inferring image segmentations using the GBP typical cut algorithm

Learning and inferring image segmentations using the GBP typical cut algorithm

Unsupervised colour image segmentation by low-level perceptual grouping

Attribute-space connectivity and connected filters

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