A Simple Stochastic Algorithm for Structural Features Learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00230194" target="_blank" >RIV/68407700:21230/15:00230194 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-16634-6_4" target="_blank" >http://dx.doi.org/10.1007/978-3-319-16634-6_4</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-16634-6_4" target="_blank" >10.1007/978-3-319-16634-6_4</a>
Alternative languages
Result language
angličtina
Original language name
A Simple Stochastic Algorithm for Structural Features Learning
Original language description
A conceptually very simple unsupervised algorithm for learning structure in the form of a hierarchical probabilistic model is described in this paper. The proposed probabilistic model can easily work with any type of image primitives such as edge segments, non-max-suppressed filter set responses, texels, distinct image regions, SIFT features, etc., and is even capable of modelling non-rigid and/or visually variable objects. The model has recursive form and consists of sets of simple and gradually growing sub-models that are shared and learned individually in layers. The proposed probabilistic framework enables to exactly compute the probability of presence of a certain model, regardless on which layer it actually is. All these learned models constitutea rich set of independent structure elements of variable complexity that can be used as features in various recognition tasks.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
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)
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the ACCV2014 Workshop: the International Workshop on Feature and Similarity Learning for Computer Vision 2014 (FSLCV 2014)
ISBN
978-3-319-16633-9
ISSN
0302-9743
e-ISSN
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Number of pages
12
Pages from-to
44-55
Publisher name
Springer
Place of publication
Cham
Event location
Singapore
Event date
Nov 1, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000362453400004