Towards Learning Hierarchical Compositional Models in the Presence of Clutter

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00212542" target="_blank" >RIV/68407700:21230/13:00212542 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-642-41181-6_54" target="_blank" >http://dx.doi.org/10.1007/978-3-642-41181-6_54</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-642-41181-6_54" target="_blank" >10.1007/978-3-642-41181-6_54</a>

Result language
angličtina
Original language name
Towards Learning Hierarchical Compositional Models in the Presence of Clutter
Original language description
Our goal is to identify hierarchical compositional models from highly cluttered data. The data to learn from are assumed to be imperfect in two respects. Firstly, large portion of the data is coming from background clutter. Secondly, data generated by arecursive compositional model are subject to random replacements of correct descendants by randomly chosen ones at every level of the hierarchy. In this paper, we study the limits and capabilities of an approach which is based on likelihood maximization.The algorithm makes explicit probabilistic assignments of individual data to compositional model and background clutter. It uses these assignments to effectively focus on the data coming from the compositional model and iteratively estimate their compositional structure.
Czech name
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Czech description
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Project
<a href="/en/project/GAP103%2F12%2F1578" target="_blank" >GAP103/12/1578: Structural and Semantic Modeling of Architecture as a Digital Image Interpretation Problem</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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