Plant classification using fractal dimension in HSV color space

In this paper, the use of fractal dimension measurements in HSV color space for image segmentation is investigated. The regions including different plant species, especially weed and non-weed, are analyzed as application. With this purpose, several features are extracted using HSV color components belonging to images, which contain different plant types, and corresponding partial fractal dimensions. Then, the extracted features are fed into Bayes classifier so that the regarding plant types are classified. Experimental results indicate that the features obtained with HSV components provide results as good as the ones that are extracted using gray scale values only. When all the features are used together, the best performance is obtained.