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ECCV
2010
Springer
12 years 9 months ago
Efficient object category recognition using classemes
We introduce a new descriptor for images which allows the construction of efficient and compact classifiers with good accuracy on object category recognition. The descriptor is the...
CLOR
2006
12 years 9 months ago
A Sparse Object Category Model for Efficient Learning and Complete Recognition
We present a "parts and structure" model for object category recognition that can be learnt efficiently and in a weakly-supervised manner: the model is learnt from examp...
Robert Fergus, Pietro Perona, Andrew Zisserman
ICPR
2008
IEEE
13 years 11 days ago
Support Vector Data Description for image categorization from Internet images
Training a classi´Čüer for object category recognition using images on the Internet is an attractive approach due to its scalability. However, a big challenge in this approach is ...
Xiaodong Yu, Daniel DeMenthon, David S. Doermann
ICPR
2010
IEEE
13 years 17 days ago
On a Quest for Image Descriptors Based on Unsupervised Segmentation Maps
This paper investigates segmentation-based image descriptors for object category recognition. In contrast to commonly used interest points the proposed descriptors are extracted f...
Piotr Koniusz, Krystian Mikolajczyk
CVPR
2010
IEEE
13 years 2 months ago
Optimizing One-Shot Recognition with Micro-Set Learning
For object category recognition to scale beyond a small number of classes, it is important that algorithms be able to learn from a small amount of labeled data per additional clas...
Kevin Tang, Marshall Tappen, Rahul Sukthankar, Chr...
ICCV
2007
IEEE
13 years 8 months ago
Active Learning with Gaussian Processes for Object Categorization
Discriminative methods for visual object category recognition are typically non-probabilistic, predicting class labels but not directly providing an estimate of uncertainty. Gauss...
Ashish Kapoor, Kristen Grauman, Raquel Urtasun, Tr...
ICCV
2007
IEEE
13 years 8 months ago
An Empirical Study of Object Category Recognition: Sequential Testing with Generalized Samples
In this paper we present an empirical study of object category recognition using generalized samples and a set of sequential tests. We study 33 categories, each consisting of a sm...
Liang Lin, Shaowu Peng, Jake Porway, Song Chun Zhu...
CVPR
2005
IEEE
13 years 8 months ago
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 semisupervised manner: the model is learnt from example ...
Robert Fergus, Pietro Perona, Andrew Zisserman
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