Learning the Semantics of Images by Using Unlabeled Samples

Authors:
Jianping Fan;Hangzai Luo;Yuli Gao
Affiliations:
University of North Carolina at Charlotte;University of North Carolina at Charlotte;University of North Carolina at Charlotte
Venue:
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Year:
2005

Citing 0
Cited 6

Large-scale video retrieval via semantic classification

MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Automatic image annotation by incorporating feature hierarchy and boosting to scale up SVM classifiers

MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Enhancing image annotation by integrating concept ontology and text-based bayesian learning model

Proceedings of the 15th international conference on Multimedia
Semantic image classification using statistical local spatial relations model

Multimedia Tools and Applications
Bayesian learning of hierarchical multinomial mixture models of concepts for automatic image annotation

CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Semi-supervised learning for image annotation based on conditional random fields

CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we have proposed a novel framework to achieve more effective classifier training by using unlabeled samples. By integrating concept hierarchy for semantic image concept organization, a hierarchical mixture model is proposed to enable multi-level image concept modeling and hierarchical classifier training. To effectively learn the base-level classifiers for the atomic image concepts at the first level of the concept hierarchy, we have proposed a novel adaptive EM algorithm to achieve more effectiveclassifier training with higher prediction accuracy. To effectively learn the classifiers for the higher-level semantic image concepts, we have also proposed a novel technique for classifier combining by using hierarchical mixture model. The experimental results on two large-scale image databases are also provided.