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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.
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.
Keyword Spotting (KWS) has been proposed as a flexible and more
error-tolerant alternative to full transcriptions. In most cases,
it allows to retrieve arbitrary query words in handwritten
historical document.This comprehensive compendium gives a
self-contained preamble and visually attractive description to the
field of graph-based KWS. The volume highlights a profound insight
into each step of the whole KWS pipeline, viz. image preprocessing,
graph representation and graph matching.Written by two
world-renowned co-authors, this unique title combines two very
current research fields of graph-based pattern recognition and
document analysis. The book serves as an attractive teaching
material for graduate students, as well as a useful reference text
for professionals, academics and researchers.
This book is concerned with a fundamentally novel approach to
graph-based pattern recognition based on vector space embedding of
graphs. It aims at condensing the high representational power of
graphs into a computationally efficient and mathematically
convenient feature vector.
This volume utilizes the dissimilarity space representation
originally proposed by Duin and Pekalska to embed graphs in real
vector spaces. Such an embedding gives one access to all algorithms
developed in the past for feature vectors, which has been the
predominant representation formalism in pattern recognition and
related areas for a long time.
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