Automatic Recognition of Wild Flowers

In this paper we propose an automatic recognition system for wild flowers. Two photos, one of the flower and one of the leaves taken from directly above or at a close angle of a single wild flower, were used as a single set. The objective (flower, leaf) is extracted from each image using a clustering method, then recognition is performed using a piecewise linear discriminant function after finding 10 features in the picture of a flower and 11 features in the picture of a leaf. We performed experiments on 20 sets of 34 species of wild flowers that grow around our university campus in the spring and early summer. The results of the experiment showed a recognition rate of 96.0% when all of the features (21) were used. Next we performed an experiment to select the features particularly useful for recognition. These results showed that six features for the pictures of flowers and two features for the pictures of leaves were the most effective, and with them a recognition rate of 96.8% could be reached. © 2003 Wiley Periodicals, Inc. Syst Comp Jpn, 34(10): 90–101, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ scj.10099