Fundamentals of speech recognition
Fundamentals of speech recognition
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Detection and Localization by Dynamic Template Warping
International Journal of Computer Vision
A class-dependent weighted dissimilarity measure for nearest neighbor classification problems
Pattern Recognition Letters
Robust Face Recognition Using Dynamic Space Warping
ECCV '02 Proceedings of the International ECCV 2002 Workshop Copenhagen on Biometric Authentication
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
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)
Prototype selection for dissimilarity-based classifiers
Pattern Recognition
Improving nearest neighbor rule with a simple adaptive distance measure
Pattern Recognition Letters
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Generalizing dissimilarity representations using feature lines
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Optimizing dissimilarity-based classifiers using a newly modified hausdorff distance
PKAW'06 Proceedings of the 9th Pacific Rim Knowledge Acquisition international conference on Advances in Knowledge Acquisition and Management
A multiple combining method for optimizing dissimilarity-based classification
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
An improvement of dissimilarity-based classifications using sift algorithm
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Dissimilarity-Based classifications in eigenspaces
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Hi-index | 0.00 |
The aim of this paper is to present a dissimilarity measure strategy by which a new philosophy for pattern classification pertaining to dissimilarity-based classifiers (DBCs) can be efficiently implemented. Proposed by Duin and his co-authors, DBCs are a way of defining classifiers among classes; they are not based on the feature measurements of individual patterns, but rather on a suitable dissimilarity measure among the patterns. The problem with this strategy is that we need to measure the inter-pattern dissimilarities for all the training samples to ensure there is no zero distance between objects of different classes. Consequently, the classes do not overlap, and therefore, the lower error bound is zero. In image classification tasks, such as face recognition, one of the most intractable problems is the distortion and lack of information caused by the differences in face directions and sizes. To overcome the above problem, in this paper, we propose a new method of measuring the dissimilarity distance between two images of an object when the images have different directions and sizes and there is no direct feature correspondence. In the proposed method, a dynamic programming technique, such as dynamic time warping, is used to overcome the limitation of one-to-one mapping. Furthermore, when determining the matching templates of two images in dynamic time warping, we use a correlation coefficient-based method. With this method, we can find an optimal warping path by surveying the images in a one-dimensional or two-dimensional way (that is, with vertical-only scanning or vertical-horizontal scanning). Our experimental results demonstrate that the proposed mechanism can improve the classification accuracy of conventional approaches for an artificial data set and two real-life benchmark databases.