Image Segmentation using Commute times

Qiu, Huaijun; Hancock, Edwin R.

doi:10.5244/c.19.94

Cited by 27 publications

(14 citation statements)

References 11 publications

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“…Almost at the same period, Qiu & Hancock [47,48], Ham, Lee, Mika & Schölkopf [23], Yen et al [67] as well as Brand [8] defined the same CT embedding, preserving the commute-time distance, and applied it to image segmentation and multi-body motion tracking [47,48], to dimensionality reduction of manifolds [23], to clustering [67] as well as to collaborative filtering [8], with interesting results. On the other hand, Zhou [70,71] uses the average first passage time between two nodes as a dissimilarity index in order to cluster them.…”

Section: Related Workmentioning

confidence: 99%

A family of dissimilarity measures between nodes generalizing both the shortest-path and the commute-time distances

Yen

Saerens

Mantrach

et al. 2008

Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

139

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This work introduces a new family of link-based dissimilarity measures between nodes of a weighted directed graph. This measure, called the randomized shortest-path (RSP) dissimilarity, depends on a parameter θ and has the interesting property of reducing, on one end, to the standard shortest-path distance when θ is large and, on the other end, to the commute-time (or resistance) distance when θ is small (near zero). Intuitively, it corresponds to the expected cost incurred by a random walker in order to reach a destination node from a starting node while maintaining a constant entropy (related to θ) spread in the graph. The parameter θ is therefore biasing gradually the simple random walk on the graph towards the shortest-path policy. By adopting a statistical physics approach and computing a sum over all the possible paths (discrete path integral), it is shown that the RSP dissimilarity from every node to a particular node of interest can be computed efficiently by solving two linear systems of n equations, where n is the number of nodes. On the other hand, the dissimilarity between every couple of nodes is obtained by inverting an n × n matrix. The proposed measure can be used for various graph mining tasks such as computing betweenness centrality, finding dense communities, etc, as shown in the experimental section.

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Section: Related Workmentioning

confidence: 99%

A family of dissimilarity measures between nodes generalizing both the shortest-path and the commute-time distances

Yen

Saerens

Mantrach

et al. 2008

Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

139

View full text Add to dashboard Cite

show abstract

“…The same commute-time embedding was defined in Ham, Lee, Mika, and Scholkopf (2004), Hancock (2005, 2007) and Yen et al (2005) as well as (Brand, 2005), preserving the commute-time distance. It was applied to image segmentation and multi-body motion tracking (Qiu & Hancock, 2005, 2007, to dimensionality reduction of manifolds (Ham et al, 2004), to clustering (Yen et al, 2005), and to collaborative filtering (Brand, 2005;Fouss et al, 2007), with interesting results. The commutetime kernel is also closely related to the ' 'Fiedler vector'' (Fiedler, 1975b;Mohar, 1992), widely used for graph partitioning (Chan, Ciarlet, & Szeto, 1997;Pothen, Simon, & Liou, 1990) and clustering (Donetti & Munoz, 2004), as detailed in Fouss et al (2007), but also to discrete Green functions (Ding, Jin, Li, & Simon, 2007).…”

Section: Related Workmentioning

confidence: 99%

An Experimental Investigation of Graph Kernels on a Collaborative Recommendation Task

Fouss

Yen

Pirotte

et al. 2006

Sixth International Conference on Data Mining (ICDM'06)

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a b s t r a c tThis paper presents a survey as well as an empirical comparison and evaluation of seven kernels on graphs and two related similarity matrices, that we globally refer to as ''kernels on graphs'' for simplicity. They are the exponential diffusion kernel, the Laplacian exponential diffusion kernel, the von Neumann diffusion kernel, the regularized Laplacian kernel, the commute-time (or resistance-distance) kernel, the randomwalk-with-restart similarity matrix, and finally, a kernel first introduced in this paper (the regularized commute-time kernel) and two kernels defined in some of our previous work and further investigated in this paper (the Markov diffusion kernel and the relative-entropy diffusion matrix). The kernel-ongraphs approach is simple and intuitive. It is illustrated by applying the nine kernels to a collaborativerecommendation task, viewed as a link prediction problem, and to a semisupervised classification task, both on several databases. The methods compute proximity measures between nodes that help study the structure of the graph. Our comparisons suggest that the regularized commute-time and the Markov diffusion kernels perform best on the investigated tasks, closely followed by the regularized Laplacian kernel.

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“…These methods define an energy function whose minimum value corresponds to the optimal segmentation, and this energy is optimized via graph optimization. Following this graphbased methods, works like [5,8] also introduce spectral clustering theory in the framework, which uses eigenvectors and eigenvalues of the similarities between pixels. Some of these techniques have been also developed in order to deal with multi-label image segmentation, where more than one objects (or parts of objects) are segmented at the same time.…”

Section: Introductionmentioning

confidence: 99%

Automatic user interaction correction via Multi-label Graph cuts

Hernández-Vela

Primo

Escalera

2011

2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)

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Most applications in image segmentation requires from user interaction in order to achieve accurate results. However, user wants to achieve the desired segmentation accuracy reducing effort of manual labelling. In this work, we extend standard multi-label α-expansion Graph Cut algorithm so that it analyzes the interaction of the user in order to modify the object model and improve final segmentation of objects. The approach is inspired in the fact that fast user interactions may introduce some pixel errors confusing object and background. Our results with different degrees of user interaction and input errors show high performance of the proposed approach on a multi-label human limb segmentation problem compared with classical α-expansion algorithm.

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Image Segmentation using Commute times

Cited by 27 publications

References 11 publications

A family of dissimilarity measures between nodes generalizing both the shortest-path and the commute-time distances

A family of dissimilarity measures between nodes generalizing both the shortest-path and the commute-time distances

An Experimental Investigation of Graph Kernels on a Collaborative Recommendation Task

Automatic user interaction correction via Multi-label Graph cuts

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