Delving into the whorl of flower segmentation

Abstract

We describe an algorithm for automatically segmenting flowers in colour photographs. This is a challenging problem because of the sheer variety of flower classes, the intra-class variability, the variation within a particular flower, and the variability of imaging conditions ‐ lighting, pose, foreshortening etc. The method couples two models ‐ a colour model for foreground and background, and a generic shape model for the petal structure. This shape model is tolerant to viewpoint changes and petal deformations, and applicable across many different flower classes. The segmentations are produced using a MRF cost function optimized using graph cuts. The algorithm is tested on 13 flower classes and more than 750 examples. Performance is assessed against ground truth segmentations.