A high-speed rough classification method based on associative matching
Masanori Toda, Yoshihide Magome, Tadahiro Kubota · Systems and Computers in Japan · 1999
There have been many attempts to identify an unknown input pattern from patterns having many categories represented by multiple-dimension feature vectors, but they require a very large computation time when a conventional method is applied. This article proposes a high-speed automatic method of determining a small number of likely categories without calculating distances to standard patterns. In the proposed method, the region of existence of samples on each feature axis is defined by using the learning-sample distribution, and the region is divided into l cells. Then a dictionary is created so that a category is given when a class is specified. The class is determined from the feature elements of an unknown input pattern. This procedure is applied to all of the feature elements so that the number of cumulative features is obtained. By taking the cumulative features from the maximum to the (maximum – k) (an integer) and applying postprocessing, a likely category is determined. The effectiveness of the proposed method has been confirmed by using 1153 on-line handwritten characters (Japanese phonetic letters and Chinese characters). © 1999 Scripta Technica, Syst Comp Jpn, 30(9): 34–43, 1999