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Graph Embedding for Pattern Analysis pp 1–26Cite as

Multilevel Analysis of Attributed Graphs for Explicit Graph Embedding in Vector Spaces

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Abstract

Ability to recognize patterns is among the most crucial capabilities of human beings for their survival, which enables them to employ their sophisticated neural and cognitive systems [1], for processing complex audio, visual, smell, touch, and taste signals. Man is the most complex and the best existing system of pattern recognition. Without any explicit thinking, we continuously compare, classify, and identify huge amount of signal data everyday [2], starting from the time we get up in the morning till the last second we fall asleep. This includes recognizing the face of a friend in a crowd, a spoken word embedded in noise, the proper key to lock the door, smell of coffee, the voice of a favorite singer, the recognition of alphabetic characters, and millions of more tasks that we perform on regular basis.

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Notes

  1. 1.

    http://www.greyc.ensicaen.fr/iapr-tc15/index.php.

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Author information

Authors and Affiliations

  1. François Rabelais University of Tours, Tours, France

    Muhammad Muzzamil Luqman & Jean-Yves Ramel

  2. Autonòma University of Barcelona, Barcelona, Spain

    Muhammad Muzzamil Luqman & Josep Lladós

Authors
  1. Muhammad Muzzamil Luqman
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  2. Jean-Yves Ramel
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  3. Josep Lladós
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Correspondence to Muhammad Muzzamil Luqman .

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Editors and Affiliations

  1. , Dept. of ECE, College of Engineering, Northeastern University, 403 Dana Research Center, 360 Huntington Ave, Boston, 02115, Massachusetts, USA

    Yun Fu

  2. Honeywell, Douglas Drive North 1985, Golden Valley, 55422, USA

    Yunqian Ma

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Luqman, M.M., Ramel, JY., Lladós, J. (2013). Multilevel Analysis of Attributed Graphs for Explicit Graph Embedding in Vector Spaces. In: Fu, Y., Ma, Y. (eds) Graph Embedding for Pattern Analysis. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4457-2_1

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  • DOI: https://doi.org/10.1007/978-1-4614-4457-2_1

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