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Two-dimensional multifractal detrended fluctuation analysis for plant identification.

Wang F, Liao DW, Li JW, Liao GP - Plant Methods (2015)

Bottom Line: The chosen three parameters form a three-dimensional space in which the samples from the same species can be clustered together and be separated from other species.The resulting averaged discriminant accuracy reaches 98.4% for every two species by the 10 - fold cross validation, while the accuracy reaches 93.96% for all fifteen species.Our method, based on the 2D MF-DFA, provides a feasible and efficient procedure to identify plant species.

View Article: PubMed Central - PubMed

Affiliation: College of Science, Hunan Agricultural University, Changsha, 410128 China.

ABSTRACT

Background: In this paper, a novel method is proposed to identify plant species by using the two- dimensional multifractal detrended fluctuation analysis (2D MF-DFA). Our method involves calculating a set of multifractal parameters that characterize the texture features of each plant leaf image. An index, I 0, that characterizes the relation of the intra-species variances and inter-species variances is introduced. This index is used to select three multifractal parameters for the identification process. The procedure is applied to the Swedish leaf data set containing leaves from fifteen different tree species.

Results: The chosen three parameters form a three-dimensional space in which the samples from the same species can be clustered together and be separated from other species. Support vector machines and kernel methods are employed to assess the identification accuracy. The resulting averaged discriminant accuracy reaches 98.4% for every two species by the 10 - fold cross validation, while the accuracy reaches 93.96% for all fifteen species.

Conclusions: Our method, based on the 2D MF-DFA, provides a feasible and efficient procedure to identify plant species.

No MeSH data available.


Related in: MedlinePlus

Multifractal nature in the power-law of the gray image of leaf MX017. (a): The plots of the detrended fluctuation function Fq(s) for different values of q. In order to make clearer contrast among the different curves, some constants are subtracted. The straight lines are the best fitted lines whose slopes are shown in the legend. (b): Dependence of τ(q) and h(q) on q.
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Fig3: Multifractal nature in the power-law of the gray image of leaf MX017. (a): The plots of the detrended fluctuation function Fq(s) for different values of q. In order to make clearer contrast among the different curves, some constants are subtracted. The straight lines are the best fitted lines whose slopes are shown in the legend. (b): Dependence of τ(q) and h(q) on q.

Mentions: For the Swedish leaf data set, we find that the leaf images all possess the multifractal nature. Figure 2 and Figure 3 demonstrate the multifractal nature of two randomly chosen leaf images, namely, image MIV004 and image MX017, the former has 1793 × 979 pixels and the latter has 2934 × 1771 pixels. In each the left panel illustrates the dependence of the detrended fluctuation function Fq(s) as a function of the scale s for different q. The well fitted straight lines indicate the evident power law scaling of Fq(s) versus s. The right panel shows that τ(q) is nonlinear in q, indicated by the fact that h(q) depends on q.Figure 2


Two-dimensional multifractal detrended fluctuation analysis for plant identification.

Wang F, Liao DW, Li JW, Liao GP - Plant Methods (2015)

Multifractal nature in the power-law of the gray image of leaf MX017. (a): The plots of the detrended fluctuation function Fq(s) for different values of q. In order to make clearer contrast among the different curves, some constants are subtracted. The straight lines are the best fitted lines whose slopes are shown in the legend. (b): Dependence of τ(q) and h(q) on q.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4358846&req=5

Fig3: Multifractal nature in the power-law of the gray image of leaf MX017. (a): The plots of the detrended fluctuation function Fq(s) for different values of q. In order to make clearer contrast among the different curves, some constants are subtracted. The straight lines are the best fitted lines whose slopes are shown in the legend. (b): Dependence of τ(q) and h(q) on q.
Mentions: For the Swedish leaf data set, we find that the leaf images all possess the multifractal nature. Figure 2 and Figure 3 demonstrate the multifractal nature of two randomly chosen leaf images, namely, image MIV004 and image MX017, the former has 1793 × 979 pixels and the latter has 2934 × 1771 pixels. In each the left panel illustrates the dependence of the detrended fluctuation function Fq(s) as a function of the scale s for different q. The well fitted straight lines indicate the evident power law scaling of Fq(s) versus s. The right panel shows that τ(q) is nonlinear in q, indicated by the fact that h(q) depends on q.Figure 2

Bottom Line: The chosen three parameters form a three-dimensional space in which the samples from the same species can be clustered together and be separated from other species.The resulting averaged discriminant accuracy reaches 98.4% for every two species by the 10 - fold cross validation, while the accuracy reaches 93.96% for all fifteen species.Our method, based on the 2D MF-DFA, provides a feasible and efficient procedure to identify plant species.

View Article: PubMed Central - PubMed

Affiliation: College of Science, Hunan Agricultural University, Changsha, 410128 China.

ABSTRACT

Background: In this paper, a novel method is proposed to identify plant species by using the two- dimensional multifractal detrended fluctuation analysis (2D MF-DFA). Our method involves calculating a set of multifractal parameters that characterize the texture features of each plant leaf image. An index, I 0, that characterizes the relation of the intra-species variances and inter-species variances is introduced. This index is used to select three multifractal parameters for the identification process. The procedure is applied to the Swedish leaf data set containing leaves from fifteen different tree species.

Results: The chosen three parameters form a three-dimensional space in which the samples from the same species can be clustered together and be separated from other species. Support vector machines and kernel methods are employed to assess the identification accuracy. The resulting averaged discriminant accuracy reaches 98.4% for every two species by the 10 - fold cross validation, while the accuracy reaches 93.96% for all fifteen species.

Conclusions: Our method, based on the 2D MF-DFA, provides a feasible and efficient procedure to identify plant species.

No MeSH data available.


Related in: MedlinePlus