This unique text/reference presents a thorough introduction to the
field of structural pattern recognition, with a particular focus on
graph edit distance (GED). The book also provides a detailed review
of a diverse selection of novel methods related to GED, and
concludes by suggesting possible avenues for future research.
Topics and features: formally introduces the concept of GED, and
highlights the basic properties of this graph matching paradigm;
describes a reformulation of GED to a quadratic assignment problem;
illustrates how the quadratic assignment problem of GED can be
reduced to a linear sum assignment problem; reviews strategies for
reducing both the overestimation of the true edit distance and the
matching time in the approximation framework; examines the
improvement demonstrated by the described algorithmic framework
with respect to the distance accuracy and the matching time;
includes appendices listing the datasets employed for the
experimental evaluations discussed in the book.
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