A multiple combining method for optimizing dissimilarity-based classification

Authors:
Sang-Woon Kim;Seunghwan Kim
Affiliations:
Dept. of Computer Science and Engineering, Myongji University, Yongin, South Korea;School of Electrical Engineering, University of Waterloo, Waterloo, ON, Canada
Venue:
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Year:
2010

Citing 14
Cited 0

Face Recognition: The Problem of Compensating for Changes in Illumination Direction

IEEE Transactions on Pattern Analysis and Machine Intelligence
On Combining Classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Detection and Localization by Dynamic Template Warping

International Journal of Computer Vision
Schaum's Outline of Introduction to Probability & Statistics: Principles & Applications for Engineering & the Computing Sciences

Schaum's Outline of Introduction to Probability & Statistics: Principles & Applications for Engineering & the Computing Sciences
Robust Face Recognition Using Dynamic Space Warping

ECCV '02 Proceedings of the International ECCV 2002 Workshop Copenhagen on Biometric Authentication
A generalized kernel approach to dissimilarity-based classification

The Journal of Machine Learning Research
Combining Pattern Classifiers: Methods and Algorithms

Combining Pattern Classifiers: Methods and Algorithms
On the Euclidean Distance of Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Affine Invariant Dynamic Time Warping and its Application to Online Rotated Handwriting Recognition

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)

The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
Prototype selection for dissimilarity-based classifiers

Pattern Recognition
On using prototype reduction schemes to optimize dissimilarity-based classification

Pattern Recognition
On Combining Dissimilarity-Based Classifiers to Solve the Small Sample Size Problem for Appearance-Based Face Recognition

CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
A Dynamic Programming Technique for Optimizing Dissimilarity-Based Classifiers

SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper reports an experimental study on a multiple combining method for optimizing dissimilarity-based classifications (DBCs) by simultaneously using a dynamic time warping (DTW) and a multiple fusion strategy (MFS). DBCs are a way of defining classifiers among classes; they are not based on the feature measurements of individual samples, but rather on a suitable dissimilarity measure among the samples. In DTW, the dissimilarity is measured in two steps: first, we adjust the object samples by finding the best warping path with a correlation coefficient-based DTW technique. We then compute the dissimilarity distance between the adjusted objects with conventional measures. In MFS, fusion strategies are repeatedly used in generating dissimilarity matrices as well as in designing classifiers: we first combine the dissimilarity matrices obtained with the DTWtechnique to a new matrix. After training some base classifiers in the new matrix, we again combine the results of the base classifiers. Our experimental results for well-known benchmark databases demonstrate that the proposed mechanism works well and achieves further improved results in terms of the classification accuracy compared with the previous approaches.