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Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR)

SSPR /SPR 2012: Structural, Syntactic, and Statistical Pattern Recognition pp 171–180Cite as

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  2. Structural, Syntactic, and Statistical Pattern Recognition
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A Relational Kernel-Based Framework for Hierarchical Image Understanding

A Relational Kernel-Based Framework for Hierarchical Image Understanding

  • Laura Antanas24,
  • Paolo Frasconi24,
  • Fabrizio Costa24,
  • Tinne Tuytelaars24 &
  • …
  • Luc De Raedt24 
  • Conference paper
  • 2443 Accesses

  • 7 Citations

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7626)

Abstract

While relational representations have been popular in early work on syntactic and structural pattern recognition, they are rarely used in contemporary approaches to computer vision due to their pure symbolic nature. The recent progress and successes in combining statistical learning principles with relational representations motivates us to reinvestigate the use of such representations. More specifically, we show that statistical relational learning can be successfully used for hierarchical image understanding. We employ kLog, a new logical and relational language for learning with kernels to detect objects at different levels in the hierarchy. The key advantage of kLog is that both appearance features and rich, contextual dependencies between parts in a scene can be integrated in a principled and interpretable way to obtain a qualitative representation of the problem. At each layer, qualitative spatial structures of parts in images are detected, classified and then employed one layer up the hierarchy to obtain higher-level semantic structures. We apply a four-layer hierarchy to street view images and successfully detect corners, windows, doors, and individual houses.

Keywords

  • Ground Atom
  • Contour Segment
  • Graph Kernel
  • Shape Grammar
  • Object Layer

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Author information

Authors and Affiliations

  1. Katholieke Universiteit Leuven, Belgium

    Laura Antanas, Paolo Frasconi, Fabrizio Costa, Tinne Tuytelaars & Luc De Raedt

Authors
  1. Laura Antanas
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  2. Paolo Frasconi
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  3. Fabrizio Costa
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  4. Tinne Tuytelaars
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  5. Luc De Raedt
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Auckland, Private Bag 92019, 1142, Auckland, New Zealand

    Georgy Gimel’farb

  2. Department of Computer Science, University of York, Deramore Lane, YO10 5GH, York, UK

    Edwin Hancock

  3. Institute of Media and Information Technology, Chiba University, Yayoi-cho 1-33, 263-8522, Inage-ku, Chiba, Japan

    Atsushi Imiya

  4. Technische Universität/Fraunhofer IGD, Fraunhoferstraße 5, 64283, Darmstadt, Germany

    Arjan Kuijper

  5. Graduate School of Information Science and Technology, Hokkaido University, 060-0814, Sapporo, Japan

    Mineichi Kudo

  6. Graduate School of Engineering, Tohoku University, 6-6-05 Aoba, Aramaki, Aoba-ku, 980-8579, Sendai, Miyagi, Japan

    Shinichiro Omachi

  7. Centre for Vision, Speech and Signal Processing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Terry Windeatt

  8. C&C Innovation Research Laboratories, NEC Corporation, 8916-47 Takayama-cho, Ikoma-Shi, Nara, Japan

    Keiji Yamada

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© 2012 Springer-Verlag Berlin Heidelberg

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Antanas, L., Frasconi, P., Costa, F., Tuytelaars, T., De Raedt, L. (2012). A Relational Kernel-Based Framework for Hierarchical Image Understanding. In: Gimel’farb, G., et al. Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2012. Lecture Notes in Computer Science, vol 7626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34166-3_19

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  • DOI: https://doi.org/10.1007/978-3-642-34166-3_19

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