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Speeding-Up Graph-Based Keyword Spotting by Quadtree Segmentations

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Computer Analysis of Images and Patterns (CAIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10424))

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Abstract

Keyword Spotting (KWS) improves the accessibility to handwritten historical documents by unconstrained retrievals of keywords. The proposed KWS framework operates on segmented words that are in turn represented as graphs. The actual KWS process is based on matching graphs by means of a cubic-time graph matching algorithm. Although this matching algorithm is quite efficient, the polynomial time complexity might still be a limiting factor (especially in case of large documents). The present paper introduces a novel approach that aims at speeding up the retrieval process. The basic idea is to first segment individual graphs into smaller subgraphs by means of a quadtree procedure. Eventually, the graph matching procedure can be conducted on the resulting pairs of smaller subgraphs. In an experimental evaluation on two benchmark datasets we empirically confirm substantial speed-ups while the KWS accuracy is nearly not affected.

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Notes

  1. 1.

    George Washington Papers at the Library of Congress, 1741–1799: Series 2, Letterbook 1, pp. 270–279 & pp. 300–309, http://memory.loc.gov/ammem/gwhtml/gwseries2.html.

  2. 2.

    Parzival at IAM historical document database, http://www.fki.inf.unibe.ch/databases/iam-historical-document-database/parzival-database.

  3. 3.

    BP stand for bipartite (LSAPs are also termed bipartite matching problem).

  4. 4.

    We carry out our experiments on a high performance computing cluster with dozens of 2.2 GHz CPU nodes. Hence, these readings refer to the average matching time per keyword measured in a sequential scenario.

References

  1. Fernandez-Mota, D., Almazan, J., Cirera, N., Fornes, A., Llados, J.: BH2M: The Barcelona historical, handwritten marriages database. In: International Conference on Pattern Recognition, pp. 256–261 (2014)

    Google Scholar 

  2. Fischer, A., Frinken, V., Fornés, A., Bunke, H.: Transcription alignment of Latin manuscripts using hidden Markov models. In: Workshop on Historical Document Imaging and Processing, New York, p. 29 (2011)

    Google Scholar 

  3. Fischer, A., Keller, A., Frinken, V., Bunke, H.: Lexicon-free handwritten word spotting using character HMMs. Pattern Recognit. Lett. 33(7), 934–942 (2012)

    Article  Google Scholar 

  4. Manmatha, R., Han, C., Riseman, E.: Word spotting: a new approach to indexing handwriting. In: Computer Vision and Pattern Recognition, pp. 631–637 (1996)

    Google Scholar 

  5. Rath, T., Manmatha, R.: Word image matching using dynamic time warping. In: Computer Vision and Pattern Recognition, vol. 2, pp. II-521–II-527 (2003)

    Google Scholar 

  6. Rodríguez-Serrano, J.A., Perronnin, F.: Handwritten word-spotting using hidden Markov models and universal vocabularies. Pattern Recognit. 42(9), 2106–2116 (2009)

    Article  MATH  Google Scholar 

  7. Rodriguez, J.A., Perronnin, F.: Local gradient histogram features for word spotting in unconstrained handwritten documents. In: International Conference on Frontiers in Handwriting Recognition, pp. 7–12 (2008)

    Google Scholar 

  8. Rodríguez-Serrano, J.A., Perronnin, F.: A model-based sequence similarity with application to handwritten word spotting. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2108–2120 (2012)

    Article  Google Scholar 

  9. Perronnin, F., Rodriguez-Serrano, J.A.: Fisher kernels for handwritten word-spotting. In: International Conference on Document Analysis and Recognition, pp. 106–110 (2009)

    Google Scholar 

  10. Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty years of graph matching in pattern recognition. Int. J. Pattern Recognit. Artif. Intell. 18(03), 265–298 (2004)

    Article  Google Scholar 

  11. Riesen, K.: Structural Pattern Recognition with Graph Edit Distance. Advances in Computer Vision and Pattern Recognition. Springer, Cham (2015). doi:10.1007/978-3-319-27252-8

    Book  MATH  Google Scholar 

  12. Stauffer, M., Tschachtli, T., Fischer, A., Riesen, K.: A survey on applications of bipartite graph edit distance. In: Foggia, P., Liu, C.-L., Vento, M. (eds.) GbRPR 2017. LNCS, vol. 10310, pp. 242–252. Springer, Cham (2017). doi:10.1007/978-3-319-58961-9_22

    Chapter  Google Scholar 

  13. Wang, P., Eglin, V., Garcia, C., Largeron, C., Llados, J., Fornes, A.: A novel learning-free word spotting approach based on graph representation. In: International Workshop on Document Analysis Systems, pp. 207–211 (2014)

    Google Scholar 

  14. Bui, Q.A., Visani, M., Mullot, R.: Unsupervised word spotting using a graph representation based on invariants. In: International Conference on Document Analysis and Recognition, pp. 616–620 (2015)

    Google Scholar 

  15. Riba, P., Llados, J., Fornes, A.: Handwritten word spotting by inexact matching of grapheme graphs. In: International Conference on Document Analysis and Recognition, pp. 781–785 (2015)

    Google Scholar 

  16. Stauffer, M., Fischer, A., Riesen, K.: Graph-based keyword spotting in historical handwritten documents. In: International Workshop on Structural, Syntactic, and Statistical Pattern Recognition (2016)

    Google Scholar 

  17. Stauffer, M., Fischer, A., Riesen, K.: A novel graph database for handwritten word images. In: International Workshop on Structural, Syntactic, and Statistical Pattern Recognition (2016)

    Google Scholar 

  18. Stauffer, M., Fischer, A., Riesen, K.: Speeding-up graph-based keyword spotting in historical handwritten documents. In: Foggia, P., Liu, C.-L., Vento, M. (eds.) GbRPR 2017. LNCS, vol. 10310, pp. 83–93. Springer, Cham (2017). doi:10.1007/978-3-319-58961-9_8

    Chapter  Google Scholar 

  19. Bunke, H., Allermann, G.: Inexact graph matching for structural pattern recognition. Pattern Recognit. Lett. 1(4), 245–253 (1983)

    Article  MATH  Google Scholar 

  20. Berretti, S., Del Bimbo, A., Vicario, E.: Efficient matching and indexing of graph models in content-based retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 23(10), 1089–1105 (2001)

    Article  Google Scholar 

  21. Fankhauser, S., Riesen, K., Bunke, H.: Speeding up graph edit distance computation through fast bipartite matching. In: Jiang, X., Ferrer, M., Torsello, A. (eds.) GbRPR 2011. LNCS, vol. 6658, pp. 102–111. Springer, Heidelberg (2011). doi:10.1007/978-3-642-20844-7_11

    Chapter  Google Scholar 

  22. Koopmans, T.C., Beckmann, M.: Assignment problems and the location of economic activities. Econometrica 25(1), 53 (1957)

    Article  MathSciNet  MATH  Google Scholar 

  23. Riesen, K., Bunke, H.: Approximate graph edit distance computation by means of bipartite graph matching. Image Vis. Comput. 27(7), 950–959 (2009)

    Article  Google Scholar 

  24. Burkard, R., Dell’Amico, M., Martello, S.: Assignment Problems (2009)

    Google Scholar 

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Acknowledgments

This work has been supported by the Hasler Foundation Switzerland.

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Authors and Affiliations

  1. Institute for Information Systems, University of Applied Sciences and Arts Northwestern Switzerland, Riggenbachstr. 16, 4600, Olten, Switzerland

    Michael Stauffer & Kaspar Riesen

  2. Department of Informatics, University of Fribourg, 1700, Fribourg, Switzerland

    Andreas Fischer

  3. Institute for Complex Systems, University of Applied Sciences and Arts Western Switzerland, 1705, Fribourg, Switzerland

    Andreas Fischer

  4. Department of Informatics, University of Pretoria, Pretoria, South Africa

    Michael Stauffer

Authors
  1. Michael Stauffer
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  2. Andreas Fischer
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  3. Kaspar Riesen
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Corresponding author

Correspondence to Michael Stauffer .

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Editors and Affiliations

  1. Linköping University, Linköping, Sweden

    Michael Felsberg

  2. Lund University, Lund, Sweden

    Anders Heyden

  3. University of Southern Denmark, Odense, Denmark

    Norbert Krüger

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Stauffer, M., Fischer, A., Riesen, K. (2017). Speeding-Up Graph-Based Keyword Spotting by Quadtree Segmentations. In: Felsberg, M., Heyden, A., Krüger, N. (eds) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science(), vol 10424. Springer, Cham. https://doi.org/10.1007/978-3-319-64689-3_25

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  • DOI: https://doi.org/10.1007/978-3-319-64689-3_25

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  • Online ISBN: 978-3-319-64689-3

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