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ICCV
2007
IEEE
16 years 9 months ago
Image Classification using Random Forests and Ferns
We explore the problem of classifying images by the object categories they contain in the case of a large number of object categories. To this end we combine three ingredients: (i...
Andrew Zisserman, Anna Bosch, Xavier Muñoz
ICCV
2005
IEEE
16 years 9 months ago
Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering
In this paper, a new learning framework?probabilistic boosting-tree (PBT), is proposed for learning two-class and multi-class discriminative models. In the learning stage, the pro...
Zhuowen Tu
ICCV
2003
IEEE
16 years 9 months ago
Using Specularities for Recognition
Recognition systems have generally treated specular highlights as noise. We show how to use these highlights as a positive source of information that improves recognition of shiny...
Margarita Osadchy, David W. Jacobs, Ravi Ramamoort...
ICCV
2003
IEEE
16 years 9 months ago
Entropy-of-likelihood Feature Selection for Image Correspondence
Feature points for image correspondence are often selected according to subjective criteria (e.g. edge density, nostrils). In this paper, we present a general, non-subjective crit...
Matthew Toews, Tal Arbel
ICIP
2008
IEEE
16 years 9 months ago
Feature-based object modelling for visual surveillance
This paper introduces a new feature-based technique for implicitly modelling objects in visual surveillance. Previous work has generally employed background subtraction and other ...
Gary Baugh, Anil C. Kokaram