Datensatz

Zusammenfassung

Apart from shape, color is the most visually prominent and perceivable feature of a flower. To use color as a feature for fine-grained
plant species classification based on flower images, its descriptor has to be
discriminative, compact, and robust against photometric variations. Therefore,
we studied state-of-the-art color description methods and evaluated
their discriminative power in an image classification pipeline. Experiments
have been performed on three flower image datasets possessing large photometric
and geometric varieties. We found that implicit photometric invariance
by pooling 11 basic colors from patches around local features allows
for robust color description outperforming explicitly photometric invariant
descriptors in most cases.