Keywords
Discriminative modelArtificial intelligencePattern recognition (psychology)Computer scienceSalientGenerative modelObject (grammar)Class (philosophy)Statistical relational learningBoosting (machine learning)Generative grammarCognitive neuroscience of visual object recognitionMachine learningRelational databaseMathematicsData mining
Affiliated Institutions
Related Publications
A Discriminative Framework for Modelling Object Classes
Here we explore a discriminative learning method on underlying generative models for the purpose of discriminating between object categories. Visual recognition algorithms learn...
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories. This approach is q...
Unsupervised Feature Learning via Non-parametric Instance Discrimination
Neural net classifiers trained on data with annotated class labels can also capture apparent visual similarity among categories without being directed to do so. We study whether...
A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition
We present a "parts and structure" model for object category recognition that can be learnt efficiently and in a semi-supervised manner: the model is learnt from example images ...
Harmony potentials for joint classification and segmentation
Hierarchical conditional random fields have been successfully applied to object segmentation. One reason is their ability to incorporate contextual information at different scal...
Publication Info
- Year
- 2007
- Type
- article
- Volume
- 77
- Issue
- 1-3
- Pages
- 175-198
- Citations
- 30
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
30
OpenAlex
Cite This
Aharon Bar-Hillel,
Daphna Weinshall
(2007).
Efficient Learning of Relational Object Class Models.
International Journal of Computer Vision
, 77
(1-3)
, 175-198.
https://doi.org/10.1007/s11263-007-0091-7
Identifiers
- DOI
- 10.1007/s11263-007-0091-7