Clustering method for image pattern recognition based on elastic matching

A technique for setting standard patterns for image pattern recognition based on elastic matching is investigated. The proposed technique is a kind of clustering techniques, which generally provide standard patterns as the centroids of the distribution of training patterns in pattern space. In conventional clustering techniques, the centroid is defined as the local center of gravity under the metric of the Euclidean distance. Contrary to this, in the proposed technique an elastic matching distance is newly employed as the metric. Thus, the same elastic matching-based metric is consistently used at the standard pattern setting stage and the recognition stage with the proposed technique, whereas different metrics are inconsistently used in those stages with the conventional technique. From experimental results, it was shown that high recognition rates can be attained with the standard patterns provided by the proposed technique because of the consistency of the metric.