Abstract
One of the bottlenecks of current recognition (and graph matching) systems is their assumption of one-to-one feature (node) correspondence. This assumption breaks down in the generic object recognition task where, for example, a collection of features at one scale (in one image) may correspond to a single feature at a coarser scale (in the second image). Generic object recognition therefore requires the ability to match features many-to-many. In this paper, we will review our progress on three independent object recognition problems, each formulated as a graph matching problem and each solving the many-to-many matching problem in a different way. First, we explore the problem of learning a 2-D shape class prototype (represented as a graph) from a set of object exemplars (also represented as graphs) belonging to the class, in which there may be no one-to-one correspondence among extracted features. Next, we define a low-dimensional, spectral encoding of graph structure and use it to match entire subgraphs whose size can be different. Finally, in very recent work, we embed graphs into geometric spaces, reducing the many-to-many graph matching problem to a weighted point matching problem, for which efficient many-to-many matching algorithms exist.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Shokoufandeh, A., Keselman, Y., Demirci, F., Macrini, D., Dickinson, S. (2006). Many-to-Many Feature Matching in Object Recognition. In: Christensen, H.I., Nagel, HH. (eds) Cognitive Vision Systems. Lecture Notes in Computer Science, vol 3948. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11414353_8
Download citation
DOI: https://doi.org/10.1007/11414353_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33971-7
Online ISBN: 978-3-540-33972-4
eBook Packages: Computer ScienceComputer Science (R0)