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» Pruning Training Sets for Learning of Object Categories
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DAGM
2008
Springer
13 years 11 months ago
An Evolutionary Approach for Learning Motion Class Patterns
This article presents a genetic learning algorithm to derive discrete patterns that can be used for classification and retrieval of 3D motion capture data. Based on boolean motion ...
Meinard Müller, Bastian Demuth, Bodo Rosenhah...
ICANN
2009
Springer
14 years 3 months ago
Multimodal Sparse Features for Object Detection
In this paper the sparse coding principle is employed for the representation of multimodal image data, i.e. image intensity and range. We estimate an image basis for frontal face i...
Martin Haker, Thomas Martinetz, Erhardt Barth
CICLING
2004
Springer
14 years 2 months ago
Automatic Learning Features Using Bootstrapping for Text Categorization
When text categorization is applied to complex tasks, it is tedious and expensive to hand-label the large amounts of training data necessary for good performance. In this paper, we...
Wenliang Chen, Jingbo Zhu, Honglin Wu, Tianshun Ya...
ICPR
2000
IEEE
14 years 1 months ago
Feature Learning for Recognition with Bayesian Networks
Many realistic visual recognition tasks are “open” in the sense that the number and nature of the categories to be learned are not initially known, and there is no closed set ...
Justus H. Piater, Roderic A. Grupen
CVPR
2005
IEEE
14 years 11 months ago
Object Recognition with Features Inspired by Visual Cortex
We introduce a novel set of features for robust object recognition. Each element of this set is a complex feature obtained by combining position- and scale-tolerant edgedetectors ...
Thomas Serre, Lior Wolf, Tomaso Poggio