@InProceedings{PauRiba2015,
author="Pau Riba
and Josep Llados
and Alicia Fornes
and Anjan Dutta",
editor="C.-L.Liu
and B.Luo
and W.G.Kropatsch
and J.Cheng",
title="Large-scale Graph Indexing using Binary Embeddings of Node Contexts",
booktitle="10th IAPR-TC15 Workshop on Graph-based Representations in Pattern Recognition",
year="2015",
publisher="Springer International Publishing",
volume="9069",
pages="208--217",
optkeywords="Graph matching",
optkeywords="Graph indexing",
optkeywords="Application in document analysis",
optkeywords="Word spotting",
optkeywords="Binary embedding",
abstract="Graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their representational power in front of classical appearance-based representations in terms of feature vectors. Retrieving a query graph from a large dataset of graphs has the drawback of the high computational complexity required to compare the query and the target graphs. The most important property for a large-scale retrieval is the search time complexity to be sub-linear in the number of database examples. In this paper we propose a fast indexation formalism for graph retrieval. A binary embedding is defined as hashing keys for graph nodes. Given a database of labeled graphs, graph nodes are complemented with vectors of attributes representing their local context. Hence, each attribute counts the length of a walk of order k originated in a vertex with label l. Each attribute vector is converted to a binary code applying a binary-valued hash function. Therefore, graph retrieval is formulated in terms of finding target graphs in the database whose nodes have a small Hamming distance from the query nodes, easily computed with bitwise logical operators. As an application example, we validate the performance of the proposed methods in a handwritten word spotting scenario in images of historical documents.",
optnote="DAG; 600.061; 602.006; 600.077",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2618), last updated on Wed, 16 Jan 2019 09:14:48 +0100",
isbn="978-3-319-18223-0",
issn="0302-9743",
doi="10.1007/978-3-319-18224-7_21",
opturl="http://www.springer.com/us/book/9783319182230",
file=":http://refbase.cvc.uab.es/files/RLF2015a.pdf:PDF"
}