A Visual Vocabulary for Flower Classification

We investigate to what extent ‘bag of visual words’ models can be used to distinguish categories which have significant visual similarity. To this end we develop and optimize a nearest neighbour classifier architecture, which is evaluated on a very challenging database of flower images. The flower categories are chosen to be indistinguishable on colour alone (for example), and have considerable variation in shape, scale, and viewpoint. We demonstrate that by developing a visual vocabulary that explicitly represents the various aspects (colour, shape, and texture) that distinguish one flower from another, we can overcome the ambiguities that exist between flower categories. The novelty lies in the vocabulary…