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Recent (Dis)similarity Measures Between Histograms for Recognizing Many Classes of Plant Leaves: An Experimental Comparison

Recent (Dis)similarity Measures Between Histograms for Recognizing Many Classes of Plant Leaves: An Experimental Comparison

Mauricio Orozco-Alzate
Copyright: © 2020 |Pages: 24
ISBN13: 9781799818397|ISBN10: 179981839X|ISBN13 Softcover: 9781799818403|EISBN13: 9781799818410
DOI: 10.4018/978-1-7998-1839-7.ch008
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MLA

Orozco-Alzate, Mauricio. "Recent (Dis)similarity Measures Between Histograms for Recognizing Many Classes of Plant Leaves: An Experimental Comparison." Pattern Recognition Applications in Engineering, edited by Diego Alexander Tibaduiza Burgos, et al., IGI Global, 2020, pp. 180-203. https://doi.org/10.4018/978-1-7998-1839-7.ch008

APA

Orozco-Alzate, M. (2020). Recent (Dis)similarity Measures Between Histograms for Recognizing Many Classes of Plant Leaves: An Experimental Comparison. In D. Burgos, M. Vejar, & F. Pozo (Eds.), Pattern Recognition Applications in Engineering (pp. 180-203). IGI Global. https://doi.org/10.4018/978-1-7998-1839-7.ch008

Chicago

Orozco-Alzate, Mauricio. "Recent (Dis)similarity Measures Between Histograms for Recognizing Many Classes of Plant Leaves: An Experimental Comparison." In Pattern Recognition Applications in Engineering, edited by Diego Alexander Tibaduiza Burgos, Maribel Anaya Vejar, and Francesc Pozo, 180-203. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-1839-7.ch008

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Abstract

The accurate identification of plant species is crucial in botanical taxonomy as well as in related fields such as ecology and biodiversity monitoring. In spite of the recent developments in DNA-based analyses for phylogeny and systematics, visual leaf recognition is still commonly applied for species identification in botany. Histograms, along with the well-known nearest neighbor rule, are often a simple but effective option for the representation and classification of leaf images. Such an option relies on the choice of a proper dissimilarity measure to compare histograms. Two state-of-the-art measures—called weighted distribution matching (WDM) and Poisson-binomial radius (PBR)—are compared here in terms of classification performance, computational cost, and non-metric/non-Euclidean behavior. They are also compared against other classical dissimilarity measures between histograms. Even though PBR gives the best performance at the highest cost, it is not significantly better than other classical measures. Non-Euclidean/non-metric nature seems to play an important role.

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