Results 1 - 10 of 12,693

One-shot learning of object categories

by Li Fei-fei, Rob Fergus, Pietro Perona - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2006
"... Learning visual models of object categories notoriously requires hundreds or thousands of training examples. We show that it is possible to learn much information about a category from just one, or a handful, of images. The key insight is that, rather than learning from scratch, one can take advant ..."
Abstract - Cited by 364 (20 self) - Add to MetaCart
Learning visual models of object categories notoriously requires hundreds or thousands of training examples. We show that it is possible to learn much information about a category from just one, or a handful, of images. The key insight is that, rather than learning from scratch, one can take

Local features and kernels for classification of texture and object categories: a comprehensive study

by J. Zhang, S. Lazebnik, C. Schmid - International Journal of Computer Vision , 2007
"... Recently, methods based on local image features have shown promise for texture and object recognition tasks. This paper presents a large-scale evaluation of an approach that represents images as distributions (signatures or histograms) of features extracted from a sparse set of keypoint locations an ..."
Abstract - Cited by 653 (34 self) - Add to MetaCart
Recently, methods based on local image features have shown promise for texture and object recognition tasks. This paper presents a large-scale evaluation of an approach that represents images as distributions (signatures or histograms) of features extracted from a sparse set of keypoint locations

Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories

by Li Fei-fei , 2004
"... Abstract — Current computational approaches to learning visual object categories require thousands of training images, are slow, cannot learn in an incremental manner and cannot incorporate prior information into the learning process. In addition, no algorithm presented in the literature has been te ..."
Abstract - Cited by 784 (16 self) - Add to MetaCart
Abstract — Current computational approaches to learning visual object categories require thousands of training images, are slow, cannot learn in an incremental manner and cannot incorporate prior information into the learning process. In addition, no algorithm presented in the literature has been

Discovering object categories in image collections

by Josef Sivic, Bryan C. Russell, Alexei A. Efros, Andrew Zisserman, William T. Freeman , 2004
"... Given a set of images containing multiple object categories, we seek to discover those categories and their image locations without supervision. We achieve this using generative models from the statistical text literature: probabilistic Latent Semantic Analysis (pLSA), and Latent Dirichlet Allocatio ..."
Abstract - Cited by 197 (12 self) - Add to MetaCart
Given a set of images containing multiple object categories, we seek to discover those categories and their image locations without supervision. We achieve this using generative models from the statistical text literature: probabilistic Latent Semantic Analysis (pLSA), and Latent Dirichlet

Learning object categories from google’s image search

by R. Fergus, L. Fei-fei, P. Perona, A. Zisserman - In Proceedings of the International Conference on Computer Vision , 2005
"... Current approaches to object category recognition require datasets of training images to be manually prepared, with varying degrees of supervision. We present an approach that can learn an object category from just its name, by uti-lizing the raw output of image search engines available on the Inter ..."
Abstract - Cited by 316 (18 self) - Add to MetaCart
Current approaches to object category recognition require datasets of training images to be manually prepared, with varying degrees of supervision. We present an approach that can learn an object category from just its name, by uti-lizing the raw output of image search engines available

Efficient Object Category Recognition Using

by Lorenzo Torresani, Martin Szummer, Andrew Fitzgibbon
"... Abstract. 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 output of a large number of weakly trained object category classifiers on the image. The trained categories a ..."
Abstract - Cited by 122 (9 self) - Add to MetaCart
Abstract. 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 output of a large number of weakly trained object category classifiers on the image. The trained categories

Towards Automatic Discovery of Object Categories

by M. Weber, M. Welling, P. Perona - Proc. of CVPR , 2000
"... We propose a method to learn heterogeneous models of object classes for visual recognition. The training images contain a preponderance of clutter and learning is unsupervised. Our models represent objects as probabilistic constellations of rigid parts (features). The variability within a class is r ..."
Abstract - Cited by 147 (12 self) - Add to MetaCart
We propose a method to learn heterogeneous models of object classes for visual recognition. The training images contain a preponderance of clutter and learning is unsupervised. Our models represent objects as probabilistic constellations of rigid parts (features). The variability within a class

Basic objects in natural categories

by Eleanor Rosch, Carolyn B. Mervis, Wayne D. Gray, David M. Johnson, Penny Boyes-braem - COGNITIVE PSYCHOLOGY , 1976
"... Categorizations which humans make of the concrete world are not arbitrary but highly determined. In taxonomies of concrete objects, there is one level of abstraction at which the most basic category cuts are made. Basic categories are those which carry the most information, possess the highest categ ..."
Abstract - Cited by 892 (1 self) - Add to MetaCart
Categorizations which humans make of the concrete world are not arbitrary but highly determined. In taxonomies of concrete objects, there is one level of abstraction at which the most basic category cuts are made. Basic categories are those which carry the most information, possess the highest

A Bayesian approach to unsupervised one-shot learning of object categories

by Li Fei-fei, Rob Fergus, Pietro Perona - In Proceedings of the 9th International Conference on Computer Vision , 2003
"... Learning visual models of object categories notoriously requires thousands of training examples; this is due to the diversity and richness of object appearance which requires models containing hundreds of parameters. We present a method for learning object categories from just a few images ( � �). ..."
Abstract - Cited by 211 (11 self) - Add to MetaCart
Learning visual models of object categories notoriously requires thousands of training examples; this is due to the diversity and richness of object appearance which requires models containing hundreds of parameters. We present a method for learning object categories from just a few images

Visual Object Category Recognition

by Robert Fergus , 2005
"... We investigate two generative probabilistic models for category-level object recognition. Both schemes are designed to learn categories with a minimum of supervision, requiring only a set of images known to contain the target category from a similar viewpoint. In both methods, learning is translatio ..."
Abstract - Cited by 13 (2 self) - Add to MetaCart
We investigate two generative probabilistic models for category-level object recognition. Both schemes are designed to learn categories with a minimum of supervision, requiring only a set of images known to contain the target category from a similar viewpoint. In both methods, learning
Results 1 - 10 of 12,693