PT Unknown
AU Pau Riba
   Josep Llados
   Alicia Fornes
   Anjan Dutta
TI Large-scale Graph Indexing using Binary Embeddings of Node Contexts
BT 10th IAPR-TC15 Workshop on Graph-based Representations in Pattern Recognition
PY 2015
BP 208
EP 217
VL 9069
DI 10.1007/978-3-319-18224-7_21
DE Graph matching; Graph indexing; Application in document analysis; Word spotting; Binary embedding
AB 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example, we validate the performance of the proposed methods in a handwritten word spotting scenario in images of historical documents.
ER