Statistical shape models of plant leaves

The shapes of plant leaves are of great importance to plant biologists and botanists, as they can help in distinguishing plant species, measuring their health, analyzing their growth patterns, and understanding relations between various species. We propose a statistical model that uses the Squared Root Velocity Function representation and a Riemannian elastic metric to model the observed variability in the shape of plant leaves. We show that under this representation, one can compute sample means and principal modes of variations and can characterize the observed shapes using probability models, such as Gaussians, on the tangent spaces at the sample means. The approach is fully automatic and does not require precomputing correspondences between the shapes. We validate these statistical models by analyzing their classification performance on standard benchmarks and show their utility as generative models for random sampling.

[1]  Haibin Ling,et al.  Shape Classification Using the Inner-Distance , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Roman Goldenberg,et al.  Behavior classification by eigendecomposition of periodic motions , 2005, Pattern Recognit..

[3]  Norman MacLeod,et al.  Generalizing and extending the eigenshape method of shape space visualization and analysis , 1999, Paleobiology.

[4]  Anuj Srivastava,et al.  Geodesics Between 3D Closed Curves Using Path-Straightening , 2006, ECCV.

[5]  Anuj Srivastava,et al.  Statistical shape analysis: clustering, learning, and testing , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Yuxuan Wang,et al.  A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network , 2007, 2007 IEEE International Symposium on Signal Processing and Information Technology.

[7]  Thomas S. Ray,et al.  LANDMARK EIGENSHAPE ANALYSIS: HOMOLOGOUS CONTOURS: LEAF SHAPE IN SYNGONIUM (ARACEAE) , 1992 .

[8]  Wei Jia,et al.  Multiscale Distance Matrix for Fast Plant Leaf Recognition , 2012, IEEE Transactions on Image Processing.

[9]  Anne Verroust-Blondet,et al.  Advanced shape context for plant species identification using leaf image retrieval , 2012, ICMR.

[10]  Hamid Laga,et al.  Landmark‐Guided Elastic Shape Analysis of Spherically‐Parameterized Surfaces , 2013, Comput. Graph. Forum.

[11]  Paolo Remagnino,et al.  Plant species identification using digital morphometrics: A review , 2012, Expert Syst. Appl..

[12]  Hamid Laga,et al.  A Riemannian Elastic Metric for Shape-Based Plant Leaf Classification , 2012, 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA).

[13]  Anuj Srivastava,et al.  Shape Analysis of Elastic Curves in Euclidean Spaces , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[15]  Sean White,et al.  Searching the World's Herbaria: A System for Visual Identification of Plant Species , 2008, ECCV.