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Type: Conference paper
Title: Randomly walking can get you lost: Graph segmentation with unknown edge weights
Author: Ackermann, H.
Scheuermann, B.
Chin, T.
Rosenhahn, B.
Citation: Proceedings of the 10th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, 2015 / Tai, X.-.C., Bae, E., Chan, T., Lysaker, M. (ed./s), vol.8932, pp.450-463
Publisher: Springer-Verlag Berlin
Issue Date: 2015
Series/Report no.: Lecture Notes in Computer Science, LNCS, vol 8932
ISBN: 9783319146119
ISSN: 0302-9743
Conference Name: 10th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2015) (13 Jan 2015 - 16 Jan 2015 : Hong Kong, China)
Statement of
Hanno Ackermann, Björn Scheuermann, Tat-Jun Chin, and Bodo Rosenhahn
Abstract: Spectral graph clustering is among the most popular algorithms for unsupervised segmentation. Applications include problems such as speech separation, segmenting motions or objects in video sequences and community detection in social media. It is based on the computation of a few eigenvectors of a matrix defining the connections between the graph nodes. In many real world applications, not all edge weights can be defined. In video sequences, for instance, not all 3d-points of the observed objects are visible in all the images. Relations between graph nodes representing the 3d-points cannot be defined if these never co-occur in the same images. It is common practice to simply assign an affinity of zero to such edges. In this article, we present a formal proof that this procedure decreases the separation between two clusters. An upper bound is derived on the second smallest eigenvalue of the Laplacian matrix. Furthermore, an algorithm to infer missing edges is proposed and results on synthetic and real image data are presented.
Rights: © Springer International Publishing Switzerland 2015
RMID: 0030024979
DOI: 10.1007/978-3-319-14612-6_33
Appears in Collections:Computer Science publications

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