Unsupervised Learning of Models for Recognition

@inproceedings{Weber2000UnsupervisedLO,
  title={Unsupervised Learning of Models for Recognition},
  author={Markus Weber and Max Welling and Pietro Perona},
  booktitle={ECCV},
  year={2000}
}
We present a method to learn object class models from unlabeled and unsegmented cluttered scenes for the purpose of visual object recognition. We focus on a particular type of model where objects are represented as flexible constellations of rigid parts (features). The variability within a class is represented by a joint probability density function (pdf) on the shape of the constellation and the output of part detectors. In a first stage, the method automatically identifies distinctive parts… 
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773 Citations
Highly Influential Citations
52
Background Citations
319
Methods Citations
208
Results Citations
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773 Citations

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Citation Type

Towards automatic discovery of object categories
  • Markus Weber, M. Welling, P. Perona
  • Computer Science
    Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662)
  • 2000
TLDR
A method to learn heterogeneous models of object classes for visual recognition that automatically identifies distinctive features in the training set and learns the set of model parameters using expectation maximization.
Unsupervised learning of models for object recognition
TLDR
A method to learn object class models from unlabeled and unsegmented cluttered cluttered scenes for the purpose of visual object recognition achieves very good classification results on human faces, cars, leaves, handwritten letters, and cartoon characters.
Object class recognition by unsupervised scale-invariant learning
TLDR
The flexible nature of the model is demonstrated by excellent results over a range of datasets including geometrically constrained classes (e.g. faces, cars) and flexible objects (such as animals).
Statistical part-based models for object category recognition
TLDR
A new method to learn statistical part-based structure models for object category recognition in a supervised manner that provides both successful classification and localization of the object within the image.
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 containing category…
Learning of Graphical Models and Efficient Inference for Object Class Recognition
TLDR
This work focuses on learning graphical models of object classes from arbitrary instances of objects by combining statistical local part detection with relations between object parts in a probabilistic network and shows performance equal or superior to dedicated face recognition approaches.
A Sparse Object Category Model for Efficient Learning and Complete Recognition
TLDR
A parts and structure model for object category recognition that can be learnt efficiently and in a weakly-supervised manner, bypassing the need for feature detectors, to give the globally optimal match within a query image.
Recognition by Probabilistic Hypothesis Construction
TLDR
A probabilistic framework for recognizing objects in images of cluttered scenes, learned from a single training image and modeled by the visual appearance of a set of features, and their position with respect to a common reference frame is presented.
Efficient Unsupervised Learning for Localization and Detection in Object Categories
TLDR
A novel method for learning templates for recognition and localization of objects drawn from categories using a generative model that allows learning to be orders of magnitude faster than previous approaches while incorporating many more features.
Multiple Object Class Detection with a Generative Model
TLDR
The performance of the proposed multi-object class detection approach is competitive to state of the art approaches dedicated to a single object class recognition problem.
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