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A factorization approach to grouping

Perona, P. and Freeman, W. (1998) A factorization approach to grouping. In: Computer Vision — ECCV'98. Lecture Notes in Computer Science. No.1406. Springer , Berlin, pp. 655-670. ISBN 978-3-540-64569-6.

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The foreground group in a scene may be ‘discovered’ and computed as a factorized approximation to the pairwise affinity of the elements in the scene. A pointwise approximation of the pairwise affinity information may in fact be interpreted as a ‘saliency’ index, and the foreground of the scene may be obtained by thresholding it. An algorithm called ‘affinity factorization’ is thus obtained which may be used for grouping. The affinity factorization algorithm is demonstrated on displays composed of points, of lines and of brightness values. Its relationship to the Shi-Malik normalized cuts algorithms is explored both analytically and experimentally. The affinity factorization algorithm is shown to be computationally efficient (O(n) floating-point operations for a scene composed of n elements) and to perform well on displays where the background is unstructured. Generalizations to solve more complex problems are also discussed.

Item Type:Book Section
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Perona, P.0000-0002-7583-5809
Additional Information:© 1998 Springer-Verlag Berlin Heidelberg. Conversations with Allen Tannenbaum, Xiaolin Feng, Max Welling, Sandro Zampieri, Jitendra Malik, Janbo Shi, Serge Belongie, Thomas Leung and Josh Tannenbaum are gratefully acknowledged. This research was in part supported by the NSF Engineering Research Center in Neuromorphic Systems Engineering at Caltech.
Funding AgencyGrant Number
Center for Neuromorphic Systems Engineering, CaltechUNSPECIFIED
Subject Keywords:Versus Versus Versus; Large Eigenvalue; Singular Vector; Versus Versus Versus Versus; Affinity Function
Series Name:Lecture Notes in Computer Science
Issue or Number:1406
Record Number:CaltechAUTHORS:20200812-133837584
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:104937
Deposited By: Tony Diaz
Deposited On:12 Aug 2020 20:51
Last Modified:16 Nov 2021 18:37

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