Semi---supervised Learning with Constraints for Multi---view Object Recognition

Authors:
Stefano Melacci;Marco Maggini;Marco Gori
Affiliations:
Department of Information Engineering, University of Siena, Siena, Italy 53100;Department of Information Engineering, University of Siena, Siena, Italy 53100;Department of Information Engineering, University of Siena, Siena, Italy 53100
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Year:
2009

Citing 10
Cited 1

Support Vector Machines for 3D Object Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts

IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition from Local Scale-Invariant Features

ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Recognition with Local Features: the Kernel Recipe

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Kernel Methods for Pattern Analysis

Kernel Methods for Pattern Analysis
In Defense of One-Vs-All Classification

The Journal of Machine Learning Research
Mercer Kernels for Object Recognition with Local Features

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
3D Object Modeling and Recognition Using Local Affine-Invariant Image Descriptors and Multi-View Spatial Constraints

International Journal of Computer Vision
Towards Multi-View Object Class Detection

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Sharing Visual Features for Multiclass and Multiview Object Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence

Multitask semi-supervised learning with constraints and constraint exceptions

ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we present a novel approach to multi---view object recognition based on kernel methods with constraints. Differently from many previous approaches, we describe a system that is able to exploit a set of views of an input object to recognize it. Views are acquired by cameras located around the object and each view is modeled by a specific classifier. The relationships among different views are formulated as constraints that are exploited by a sort of collaborative learning process. The proposed approach applies the constraints on unlabeled data in a semi---supervised framework. The results collected on the COIL benchmark show that constraint based learning can improve the quality of the recognition system and of each single classifier, both on the original and noisy data, and it can increase the invariance with respect to object orientation.