SKELETON BASED APPROACH FOR FLOWER CLASSIFICATION

In this paper, we present an effective system for recognizing flower images taken by digital cameras. A flower image is segmented by eliminating the background using a Iterated Graph Cut method. We obtained the skeleton from the segmented flower images using skeleton pruning method. The shape context feature are extracted from skeleton of flower images. In this work, nearest neighbor is used as a classifier. To corroborate the efficacy of the proposed method, an experiment was conducted on our own data set of 30 classes of flowers, containing 3000 samples. The data set has different flower species with similar appearance (small inter-class variations) across different classes and varying appearance (large intra-class variations) within a class. In addition, the images of flowers are of different poses, with cluttered background under different lighting and climatic conditions. An experiment has been conducted by picking images randomly from the database, it is shown that relatively a good performance can be achieved, using shape context features with the nearest neighbor classifier algorithm.