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http://dx.doi.org/10.13088/jiis.2012.18.3.001

Performance Evaluations for Leaf Classification Using Combined Features of Shape and Texture  

Kim, Seon-Jong (Department of Applied IT Engineering, Pusan National University)
Kim, Dong-Pil (Department of Landscape Architecture, Pusan National University)
Publication Information
Journal of Intelligence and Information Systems / v.18, no.3, 2012 , pp. 1-12 More about this Journal
Abstract
There are many trees in a roadside, parks or facilities for landscape. Although we are easily seeing a tree in around, it would be difficult to classify it and to get some information about it, such as its name, species and surroundings of the tree. To find them, you have to find the illustrated books for plants or search for them on internet. The important components of a tree are leaf, flower, bark, and so on. Generally we can classify the tree by its leaves. A leaf has the inherited features of the shape, vein, and so on. The shape is important role to decide what the tree is. And texture included in vein is also efficient feature to classify them. This paper evaluates the performance of a leaf classification system using both shape and texture features. We use Fourier descriptors for shape features, and both gray-level co-occurrence matrices and wavelets for texture features, and used combinations of such features for evaluation of images from the Flavia dataset. We compared the recognition rates and the precision-recall performances of these features. Various experiments showed that a combination of shape and texture gave better results for performance. The best came from the case of a combination of features of shape and texture with a flipped contour for a Fourier descriptor.
Keywords
Leaf Classification; Shape and Texture; Fourier descriptor; GLCM; Wavelet;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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