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Author (up) Pau Riba; Josep Llados; Alicia Fornes; Anjan Dutta edit  url
  Title Large-scale graph indexing using binary embeddings of node contexts for information spotting in document image databases Type Journal Article
  Year 2017 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 87 Issue Pages 203-211  
  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. However, retrieving a query graph from a large dataset of graphs implies a high computational complexity. The most important property for a large-scale retrieval is the search time complexity to be sub-linear in the number of database examples. With this aim, in this paper we propose a graph indexation formalism applied to visual 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. Then, 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the performance of the proposed methods in different real scenarios such as handwritten word spotting in images of historical documents or symbol spotting in architectural floor plans.  
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  Area Expedition Conference  
  Notes DAG; 600.097; 602.006; 603.053; 600.121 Approved no  
  Call Number RLF2017b Serial 2873  
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