Classification of leaf images

There are tremendous content‐based retrieval systems. Most of them are applied to general image databases. Some were proposed for specified databases such as texture databases, ancient paintings, document image databases, digital mammography, face image databases, etc. However, there are fewer for plant databases. Plants are used in various fields such as in foodstuff, medicine, and industry. Recently, plant is important for environment protection. On the other hand, the problem of plant destruction becomes worse in the few years. We should train people to know about plants, in turn, to treasure and protect them. In addition to the limited number of expert botanists, the convenient content‐based retrieval system for plant is necessary and useful, since it can retrieve related information and knowledge from plant database for the query leaf so as to facilitate fast learning of plants. In this study, a leaf database is constructed and a classification method for leaves is proposed. Most approaches for leaf classification in literature used contour‐based features. The proposed method tries to use region‐based features. The reasons are that region‐based features are more robust than contour‐based features since significant curvature points are hard to find. Those features adopted include aspect ratio, compactness, centroid, and horizontal/vertical projections. The effectiveness of the proposed method has been demonstrated by various experiments. On the average, our method has the classification accuracy for 1‐NN rule as 82.33% and the recall rate for 10 returned images as 48.2%, while the contour‐based method has 37.6% and 21.7%, respectively. © 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 16, 15–23, 2006