TY - GEN
T1 - Constrained AdaBoost for Totally-Ordered Global Features
AU - Ogata, Ryota
AU - Mori, Minoru
AU - Frinken, Volkmar
AU - Uchida, Seiichi
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/12/9
Y1 - 2014/12/9
N2 - This paper proposes a constrained AdaBoost algorithm for utilizing global features in a dynamic time warping (DTW) framework. Global features are defined as a spatial relationship between temporally-distant points of a temporal pattern and are useful to represent global structure of the pattern. An example is the spatial relationship between the first and the last points of a handwritten pattern of the digit '0'. Those temporally-distant points should be spatially close enough to form a closed circle, whereas those points of '6' should be distant enough. For a temporal pattern of an N-point sequence, it is possible to have N(N - 1)/2 global features. One problem of using the global features is that they are not ordered as a one dimensional sequence any more. Consequently, it is impossible to use them in a left-to-right Markovian model, such as DTW and HMM. The proposed constrained AdaBoost algorithm can select a totally-ordered subset from the set of N(N - 1)/2 global features. Since the totally-ordered features can be arranged as a one-dimensional sequence, they can be incorporated into a DTW framework for compensating nonlinear temporal fluctuation. Since the selection is governed by the AdaBoost framework, the selected features can retain discriminative power.
AB - This paper proposes a constrained AdaBoost algorithm for utilizing global features in a dynamic time warping (DTW) framework. Global features are defined as a spatial relationship between temporally-distant points of a temporal pattern and are useful to represent global structure of the pattern. An example is the spatial relationship between the first and the last points of a handwritten pattern of the digit '0'. Those temporally-distant points should be spatially close enough to form a closed circle, whereas those points of '6' should be distant enough. For a temporal pattern of an N-point sequence, it is possible to have N(N - 1)/2 global features. One problem of using the global features is that they are not ordered as a one dimensional sequence any more. Consequently, it is impossible to use them in a left-to-right Markovian model, such as DTW and HMM. The proposed constrained AdaBoost algorithm can select a totally-ordered subset from the set of N(N - 1)/2 global features. Since the totally-ordered features can be arranged as a one-dimensional sequence, they can be incorporated into a DTW framework for compensating nonlinear temporal fluctuation. Since the selection is governed by the AdaBoost framework, the selected features can retain discriminative power.
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U2 - 10.1109/ICFHR.2014.72
DO - 10.1109/ICFHR.2014.72
M3 - Conference contribution
AN - SCOPUS:84942243387
T3 - Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR
SP - 393
EP - 398
BT - Proceedings - 14th International Conference on Frontiers in Handwriting Recognition, ICFHR 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th International Conference on Frontiers in Handwriting Recognition, ICFHR 2014
Y2 - 1 September 2014 through 4 September 2014
ER -