Sage Journals: Discover world-class research

Abstract

In the biology field of botany, leaf shape recognition is an important task. One way of characterising the leaf shape is through the centroid contour distances (CCD). Each CCD path might have different resolution, so normalisation is done by associating each contour to a circular density. Densities are rotated by subtracting the mean or mode preferred direction. Distance measures between densities are used to produce a hierarchical clustering method to cluster the leaves. We illustrate our approach with a motivating small dataset as well as a larger dataset.

Keywords

circular density center contour distance hierarchical clustering time series clustering.

Get full access to this article

View all access options for this article.

References

Cope

J. S.

Corney

Clark

J. Y.

Remagnino

Wilkin

(2012). Plant Species Identification Using Digital Morphometrics: A Review. Expert Systems with Applications, 39, 7562–7573. 10.1016/j.eswa.2012.01.073

Ding

Yan

Zhang

J. J.

Xun

L. N.

Zheng

C. H.

(2016). Leaf clustering based on sparse subspace clustering. In D. S. Huang & K. H. Jo (Eds.), Intelligent computing theories and application. Springer.

Glasbey

C. A.

(1987). Complete Linkage As a Multiple Stopping Rule for Single Linkage Clustering. Journal of Classification, 4, 103–109. 10.1007/BF01890078

Hasim

Herdiyeni

Douady

(2016). Leaf Shape Recognition Using Centroid Contour Distance. IOP Conference Series: Earth and Environmental Science, 31, 012002.

Jammalamadaka

S. R.

SenGupta

(2001). Topics in Circular Statistics. World Scientific.

Johnson

R. A.

Wichern

D. W.

(2007). Applied Multivariate Statistical Analysis. Pearson.

Kadir

Nugroho

L. E.

Susanto

Santosa

P. I.

(2011). Leaf Classification Using Shape, Color, and Texture Features. International Journal of Computer Trends and Technology, 0, 225–230.

Mauseth

J. D.

(2019). Botany: An Introduction to Plant Biology. Jones and Bartlett Publishers.

Nieto-Barajas

L. E.

Contreras-Cristán

(2014). A Bayesian Nonparametric Approach for Time Series Clustering. Bayesian Analysis, 9, 147–170. 10.1214/13-BA852

10.

Phamtoan

Vovan

(2020). Improving Fuzzy Clustering Algorithm for Probability Density Functions and Applying in Image Recognition. Model Assisted Statistics and Applications, 15, 249–261. 10.3233/MAS-200492

11.

R Core Team. (2025). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

12.

Teng

C. H.

Kuo

Y. T.

Chen

Y. S.

(2009). Leaf segmentation, its 3D position estimation and leaf classification from a few images with very close viewpoints. In M. Kamel, & A. Campilho (Eds.), Image analysis and recognition. ICIAR 2009. Lecture Notes in Computer Science, Vol. 5627. Springer.

13.

Wang

Chi

Feng

Wang

(2000). Leaf image retrieval with shape features. In R. Laurini (Ed.), Advances in visual information systems. VISUAL 2000. Lecture Notes in Computer Science, Vol. 1929. Springer.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.36 MB