Shared feature extraction for semi-supervised image classification

Authors:
Yong Luo;Dacheng Tao;Bo Geng;Chao Xu;Stephen Maybank
Affiliations:
Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China;Faculty of Engineering and Information Technology, University of Technology, Sydney, Sydney, Australia;Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China;Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, China;Department of Computer Science and Information Systems, Birkbeck College, London, London, United Kingdom
Venue:
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Year:
2011

Citing 11
Cited 1

Multitask Learning

Machine Learning - Special issue on inductive transfer
Combining labeled and unlabeled data with co-training

COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data

The Journal of Machine Learning Research
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples

The Journal of Machine Learning Research
Extracting shared subspace for multi-label classification

Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
The MIR flickr retrieval evaluation

MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
A model of inductive bias learning

Journal of Artificial Intelligence Research
Unified video annotation via multigraph learning

IEEE Transactions on Circuits and Systems for Video Technology
Ranking model adaptation for domain-specific search

Proceedings of the 18th ACM conference on Information and knowledge management
Beyond distance measurement: constructing neighborhood similarity for video annotation

IEEE Transactions on Multimedia - Special section on communities and media computing
Sharing features: efficient boosting procedures for multiclass object detection

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition

Multi-class learning from class proportions

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Multi-task learning (MTL) plays an important role in image analysis applications, e.g. image classification, face recognition and image annotation. That is because MTL can estimate the latent shared subspace to represent the common features given a set of images from different tasks. However, the geometry of the data probability distribution is always supported on an intrinsic image sub-manifold that is embedded in a high dimensional Euclidean space. Therefore, it is improper to directly apply MTL to multiclass image classification. In this paper, we propose a manifold regularized MTL (MRMTL) algorithm to discover the latent shared subspace by treating the high-dimensional image space as a sub-manifold embedded in an ambient space. We conduct experiments on the PASCAL VOC'07 dataset with 20 classes and the MIR dataset with 38 classes by comparing MRMTL with conventional MTL and several representative image classification algorithms. The results suggest that MRMTL can properly extract the common features for image representation and thus improve the generalization performance of the image classification models.