Flower Classification using Combined a* b* Color and Fractal-based Texture Feature

Flower classification is a useful way for grouping a flower in certain class using specific features. This research propose a new method of flower classification system using combination of color and texture features. The first phase is getting the crown of the flower, which is localized from a flower image by using pillbox filtering and OTSU’s thresholding. In the next phase, color and texture features are extracted from the crown. The color features are extracted by removing L channel in L*a*b* color space, and taking only a* and b* channel, because of ignoring different lighting condition in flower image. The texture features are extracted by Segmentation-based Fractal Texture Analysis (SFTA). The combination features which are consisted of 10 color features and 48 texture features are used as input in k-Nearest Neighbor (kNN) classifier method with cosine distance. The flower classification achieves the best result with accuracy 73.63%.