loading
Papers

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Hakan Cevikalp 1 and Roberto Paredes 2

Affiliations: 1 Eskisehir Osmangazi University, Turkey ; 2 Universidad Politecnica de Valencia, Spain

ISBN: 978-989-8111-69-2

Keyword(s): Dimensionality reduction, Image segmentation, Metric learning, Pairwise constraints, Semi-supervised learning, Visual object classification.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Feature Extraction ; Features Extraction ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Image and Video Analysis ; Informatics in Control, Automation and Robotics ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing, Sensors, Systems Modeling and Control ; Soft Computing

Abstract: This paper describes a semi-supervised distance metric learning algorithm which uses pairwise equivalence (similarity and dissimilarity) constraints to discover the desired groups within high-dimensional data. As opposed to the traditional full rank distance metric learning algorithms, the proposed method can learn nonsquare projection matrices that yield low rank distance metrics. This brings additional benefits such as visualization of data samples and reducing the storage cost, and it is more robust to overfitting since the number of estimated parameters is greatly reduced. Our method works in both the input and kernel induced-feature space, and the distance metric is found by a gradient descent procedure that involves an eigen-decomposition in each step. Experimental results on high-dimensional visual object classification problems show that the computed distance metric improves the performance of the subsequent clustering algorithm.

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 34.236.216.93

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Cevikalp H.; Paredes R. and (2009). SEMI-SUPERVISED DISTANCE METRIC LEARNING FOR VISUAL OBJECT CLASSIFICATION.In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 315-322. DOI: 10.5220/0001768903150322

@conference{visapp09,
author={Hakan Cevikalp and Roberto Paredes},
title={SEMI-SUPERVISED DISTANCE METRIC LEARNING FOR VISUAL OBJECT CLASSIFICATION},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={315-322},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001768903150322},
isbn={978-989-8111-69-2},
}

TY - CONF

JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)
TI - SEMI-SUPERVISED DISTANCE METRIC LEARNING FOR VISUAL OBJECT CLASSIFICATION
SN - 978-989-8111-69-2
AU - Cevikalp, H.
AU - Paredes, R.
PY - 2009
SP - 315
EP - 322
DO - 10.5220/0001768903150322

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.