Shape-only Features for Plant Leaf Identification
This addresses the problem of efficient leaf identification for mobile deployment, but it is incremental as it matches existing methods that use additional features.
The paper tackled plant leaf identification using only shape features, achieving over 90% classification accuracy on most datasets and top-four accuracy over 98%, with demonstrated rotation and scale invariance.
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.