  In this work, we propose a Triangle based approach to classify flower images.
Initially, flowers are segmented using whorl based region merging segmentation.
Skeleton of a flower is obtained from the segmented flower using a skeleton
pruning method. The Delaunay triangulation is obtained from the endpoints and
junction points detected on the skeleton. The length and angle features are
extracted from the obtained Delaunay triangles and then are aggregated to
represent in the form of interval-valued type data. A suitable classifier has
been explored for the purpose of classification. To corroborate the efficacy of
the proposed method, an experiment is conducted on our own data set of 30
classes of flowers, containing 3000 samples.
