  This paper presents a novel feature set for shape-only leaf identification
motivated by real-world, mobile deployment. The feature set includes basic
shape features, as well as signal features extracted from local area integral
invariants (LAIIs), similar to curvature maps, at multiple scales. The proposed
methodology is evaluated on a number of publicly available leaf datasets with
comparable results to existing methods which make use of colour and texture
features in addition to shape. Over 90% classification accuracy is achieved on
most datasets, with top-four accuracy for these datasets reaching over 98%.
Rotation and scale invariance of the proposed features are demonstrated, along
with an evaluation of the generalisability of the approach for generic shape
matching.
