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» Classification of symbolic objects: A lazy learning approach
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ICCV
2005
IEEE
14 years 9 months ago
A Maximum Entropy Framework for Part-Based Texture and Object Recognition
This paper presents a probabilistic part-based approach for texture and object recognition. Textures are represented using a part dictionary found by quantizing the appearance of ...
Svetlana Lazebnik, Cordelia Schmid, Jean Ponce
CLOR
2006
13 years 11 months ago
A Discriminative Framework for Texture and Object Recognition Using Local Image Features
This chapter presents an approach for texture and object recognition that uses scale- or affine-invariant local image features in combination with a discriminative classifier. Text...
Svetlana Lazebnik, Cordelia Schmid, Jean Ponce
SPATIALCOGNITION
2000
Springer
13 years 11 months ago
Inference and Visualization of Spatial Relations
We present an approach to spatial inference which is based on the procedural semantics of spatial relations. In contrast to qualitative reasoning, we do not use discrete symbolic m...
Sylvia Wiebrock, Lars Wittenburg, Ute Schmid, Frit...
ESWS
2008
Springer
13 years 9 months ago
Adding Data Mining Support to SPARQL Via Statistical Relational Learning Methods
Exploiting the complex structure of relational data enables to build better models by taking into account the additional information provided by the links between objects. We exten...
Christoph Kiefer, Abraham Bernstein, André ...
ICCV
2009
IEEE
13 years 5 months ago
A hybrid generative/discriminative classification framework based on free-energy terms
Hybrid generative-discriminative techniques and, in particular, generative score-space classification methods have proven to be valuable approaches in tackling difficult object or...
Alessandro Perina, Marco Cristani, Umberto Castell...