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
Learning People Trajectories Using Semi-directional Statistics
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
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
Boundary-based lower-bound functions for dynamic time warping and their indexing
Information Sciences: an International Journal
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Dynamic Time Warping (DTW) has been widely used to align and compare two sequences. DTW can efficiently deal with local warp or deformation between sequences. However, it can't take account of affine transformation of sequences, such as rotation, shift and scale. This paper introduces a novel Affine Invariant Dynamic Time Warping (AI-DTW) method, which tries to deal with the affine transformation and sequence alignment in a unified framework. We propose an iterative algorithm to estimate the optimal transformation matrix and warping path by mutually updating them. Recognition experiments on the online rotated handwritten data illustrated that the AI-DTW achieves a recognition rate of 95.54%, which is significantly higher than that (65.87%) of the classical DTW method.