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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

The average accuracies of the 15 species for the selected combinations with increasing K.
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Fig13: The average accuracies of the 15 species for the selected combinations with increasing K.

Mentions: In this section, we test our proposed method to demonstrate its efficiency. More precisely, we test the validity of the optimal multifractal parameter combination {h(−3), αmin, Δα}. To this end, we choose other four combinations composed by three multifractal parameters to construct four three-dimensional spaces from Table 1. These four choices are {h(−3), Δf, D1}, {h(2), h(3), αmin}, {h(2), Δα, Δf} and {h(1), h(2), Δf}. One notes that each of the first three combinations contains one multifractal parameter from {h(−3), αmin, Δα} and the fourth combination consists of the three parameters that produce the three smallest I0 values. As in the procedure proposed in the previous subsection, we place the 1125 leaf samples into the four new three-dimensional spaces and also use the SVMKM to distinguish them. Under the K − fold cross validation, the discriminant accuracies with increasing K are shown in Figure 13. Obviously, the highest accuracy still comes from the combination {h(−3), αmin, Δα} for each K and the lowest accuracy comes from the combination {h(1), h(2), Δf}. This again suggests that the index I0 successfully indicates the optimal multifractal parameter combination.Figure 13


Two-dimensional multifractal detrended fluctuation analysis for plant identification.

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

The average accuracies of the 15 species for the selected combinations with increasing K.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig13: The average accuracies of the 15 species for the selected combinations with increasing K.
Mentions: In this section, we test our proposed method to demonstrate its efficiency. More precisely, we test the validity of the optimal multifractal parameter combination {h(−3), αmin, Δα}. To this end, we choose other four combinations composed by three multifractal parameters to construct four three-dimensional spaces from Table 1. These four choices are {h(−3), Δf, D1}, {h(2), h(3), αmin}, {h(2), Δα, Δf} and {h(1), h(2), Δf}. One notes that each of the first three combinations contains one multifractal parameter from {h(−3), αmin, Δα} and the fourth combination consists of the three parameters that produce the three smallest I0 values. As in the procedure proposed in the previous subsection, we place the 1125 leaf samples into the four new three-dimensional spaces and also use the SVMKM to distinguish them. Under the K − fold cross validation, the discriminant accuracies with increasing K are shown in Figure 13. Obviously, the highest accuracy still comes from the combination {h(−3), αmin, Δα} for each K and the lowest accuracy comes from the combination {h(1), h(2), Δf}. This again suggests that the index I0 successfully indicates the optimal multifractal parameter combination.Figure 13

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