TY  - CONF
AU  - Pau Riba
AU  - Josep Llados
AU  - Alicia Fornes
AU  - Anjan Dutta
A2  - GbRPR
ED  - C.-L.Liu
ED  - B.Luo
ED  - W.G.Kropatsch
ED  - J.Cheng
PY  - 2015//
TI  - Large-scale Graph Indexing using Binary Embeddings of Node Contexts
T2  - LNCS
BT  - 10th IAPR-TC15 Workshop on Graph-based Representations in Pattern Recognition
SP  - 208
EP  - 217
VL  - 9069
PB  - Springer International Publishing
KW  - Graph matching
KW  - Graph indexing
KW  - Application in document analysis
KW  - Word spotting
KW  - Binary embedding
N2  - 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example, we validate the performance of the proposed methods in a handwritten word spotting scenario in images of historical documents.
SN  - 0302-9743
SN  - 978-3-319-18223-0
UR  - http://www.springer.com/us/book/9783319182230
L1  - http://refbase.cvc.uab.es/files/RLF2015a.pdf
UR  - http://dx.doi.org/10.1007/978-3-319-18224-7_21
N1  - DAG; 600.061; 602.006; 600.077
ID  - Pau Riba2015
ER  -