Can Walking and Measuring Along Chord Bunches Better Describe Leaf Shapes?

Bin Wang, Yongsheng Gao, Changming Sun, Michael Blumenstein, John La Salle; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 6119-6128

Effectively describing and recognizing leaf shapes under arbitrary deformations, particularly from a large database, remains an unsolved problem. In this research, we attempted a new strategy of describing shape by walking along a bunch of chords that pass through the shape to measure the regions trespassed. A novel chord bunch walks (CBW) descriptor is developed through the chord walking that effectively integrates the shape image function over the walked chord to reflect the contour features and the inner properties of the shape. For each contour point, the chord bunch groups multiple pairs of chord walks to build a hierarchical framework for a coarse-to-fine description. The proposed CBW descriptor is invariant to rotation, scaling, translation, and mirror transforms. Instead of using the expensive optimal correspondence based matching, an improved Hausdorff distance encoded correspondence information is proposed for efficient yet effective shape matching. In experimental studies, the proposed method obtained substantially higher accuracies with low computational cost over the benchmarks, which indicates the research potential along this direction.