Learning visual ideals

  • M. Burge
  • W. Burger
Session 10: Recognition & Reconstruction
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1311)

Abstract

We address the problem of describing, recognizing, and learning generic, free-form objects in real-world scenes. We introduce a hybrid appearance-based approach, IDEAL, where objects are encoded as a loose collections of parts and the relations between them. The key features of this new approach are the structural part decomposition combining multi-scale wavelet segmentation and hierarchical blobs, and learning to recognize generic object categories, exhibiting large intra-class variability, from real examples with automatic model acquisition.

Keywords

Part Path Segment Segment Blob Feature Evidence Vector Generic Object Category 
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.

References

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    W. Bischof and T. Caelli. Learning structural descriptions: a new technique for conditional clustering and rule generation. PR, pages 689–697, 1994.Google Scholar
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    M. Burge, W. Burger, and W. Mayr. Learning to recognize generic visual categories using a hybrid structural approach. In ICIP-96, v 2, pp 321–324, Switzerland, 1996.CrossRefGoogle Scholar
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    M. Burge, W. Burger, and W. Mayr. Recognition and learning with polymorphic structural components. In 13th ICPR, v 1, pp 19–23, Vienna, Austria, 1996.Google Scholar
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    W. Burger and B. Bhanu. Signal-to-symbol conversion for structural object recognition using hidden Markov models. In Proc. ARPA Image Understanding Workshop, pages 1287–1291, Monterey, CA, 1994.Google Scholar
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    T. Lindeberg. Detecting salient blob-like image structures and their scales with a scale-space primal sketch. IJCV, 11(3):283–318, 1993.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • M. Burge
    • 1
  • W. Burger
    • 1
  1. 1.Department of Systems Science Computer Vision LaboratoryJohannes Kepler UniversityLinzAustria

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