This paper describes a method for recognizing sequential patterns with nonlinear time warping. The proposed method uses a sequence of local classifiers, each of which is prepared to provide a recognition result (i.e., class label) at a certain sample point. In addition, in order to compensate nonlinear time warping, the local classifier of the point v has to be assigned to the point t_v of the prototype sequential pattern. Consequently, we must solve the optimal labeling problem and the optimal point-to-point correspondence problem (i.e., the optimal mapping from v to t_v) simultaneously. In the proposed method, this multiple optimization problem is tackled by graph cut. Specifically, the α-expansion algorithm, which is an approximation algorithm for graph cut problems, is employed. After the solving the problem, the input pattern is recognized based on majority voting of the class labels obtained at the local classifiers. Several penalties are introduced for forcing neighboring local classifiers to have the same class labels and continuous point-to-point correspondence. For observing the validity of the proposed method, it was applied to an online character recognition task.
|Translated title of the contribution||Sequential Pattern Recognition by Local Classifiers and Dynamic Time Warping|
|Number of pages||8|
|Journal||IEICE technical report|
|Publication status||Published - Aug 29 2010|