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» Objective reduction using a feature selection technique
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ICCV
2011
IEEE
12 years 9 months ago
Actively Selecting Annotations Among Objects and Attributes
We present an active learning approach to choose image annotation requests among both object category labels and the objects’ attribute labels. The goal is to solicit those labe...
Adriana Kovashka, Sudheendra Vijayanarasimhan, Kri...
CVPR
2007
IEEE
14 years 11 months ago
Adaptive Patch Features for Object Class Recognition with Learned Hierarchical Models
We present a hierarchical generative model for object recognition that is constructed by weakly-supervised learning. A key component is a novel, adaptive patch feature whose width...
Fabien Scalzo, Justus H. Piater
GRAPHITE
2007
ACM
14 years 1 months ago
An evaluation of virtual lenses for object selection in augmented reality
This paper reports the results of an experiment to compare three different selection techniques in a tabletop tangible augmented reality interface. Object selection is an importan...
Julian Looser, Mark Billinghurst, Raphael Grasset,...
CIVR
2008
Springer
279views Image Analysis» more  CIVR 2008»
13 years 11 months ago
Semi-supervised learning of object categories from paired local features
This paper presents a semi-supervised learning (SSL) approach to find similarities of images using statistics of local matches. SSL algorithms are well known for leveraging a larg...
Wen Wu, Jie Yang
GECCO
2004
Springer
144views Optimization» more  GECCO 2004»
14 years 2 months ago
Feature Subset Selection, Class Separability, and Genetic Algorithms
Abstract. The performance of classification algorithms in machine learning is affected by the features used to describe the labeled examples presented to the inducers. Therefore,...
Erick Cantú-Paz