Recognition and Learning with Polymorphic Structural Components

Mark Burge, Wilhelm Burger, Wolfgang Mayr


We address the problem of describing, recognizing, and learning generic, free-form objects in real-world scenes. For this purpose, we have developed a hybrid appearance-based approach where objects are encoded as loose collections of parts and relations between neighboring parts. The key features of this approach are: part decomposition based on local structure segmentation derived from multi-scale wavelet filters, flexible and efficient recognition by combining weak structural constraints, and learning and generalization of generic object categories (with possibly large intra-class variability) from real examples.


recognition, learning, polymorphic structural components, Gabor probe structural description

Full Text:


Creative Commons License
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.

Crossref Similarity Check logo

Crossref logologo_doaj

 Hrvatski arhiv weba logo