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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

Feature descriptors of the 15 species and clustering result based on them.(a): Visualization of averaged indicators over 75 samples in each tree species in the {h(−3), αmin, Δα} space; (b): Clustering analysis result on the 15 tree species.
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Fig10: Feature descriptors of the 15 species and clustering result based on them.(a): Visualization of averaged indicators over 75 samples in each tree species in the {h(−3), αmin, Δα} space; (b): Clustering analysis result on the 15 tree species.

Mentions: For each species, the averaged {h(−3), αmin, Δα} of the 75 samples is represented by a single point in the three-dimensional parameter space (see Figure 10) in which different points representing different species may be clustered into several groups. We use the calculated discriminant accuracy of every two species as the distance between these two points (species). This allows us to conduct a cluster analysis for all samples of the 15 species by the method of hierarchical clustering [37]. The result is given in Figure 10(b), which suggests that the 15 tree species’ leaf samples can be clustered into five groups: (i) {Ulmus carpinifolia, Salix aurita, Ulmus glabra, Salix sinerea, Fagus silvatica}; (ii) {Betula pubescens, Populus, Sorbus intermedia}; (iii) {Quercus, Alnus incana, Salix alba Sericea, Populus tremula}; (iv) {Acer, Tilia} and (v) {Sorbus aucuparia}. This is consistent with visualizing the images directly from Figure 1 showing our proposed approach is applicable.Figure 10


Two-dimensional multifractal detrended fluctuation analysis for plant identification.

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

Feature descriptors of the 15 species and clustering result based on them.(a): Visualization of averaged indicators over 75 samples in each tree species in the {h(−3), αmin, Δα} space; (b): Clustering analysis result on the 15 tree species.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig10: Feature descriptors of the 15 species and clustering result based on them.(a): Visualization of averaged indicators over 75 samples in each tree species in the {h(−3), αmin, Δα} space; (b): Clustering analysis result on the 15 tree species.
Mentions: For each species, the averaged {h(−3), αmin, Δα} of the 75 samples is represented by a single point in the three-dimensional parameter space (see Figure 10) in which different points representing different species may be clustered into several groups. We use the calculated discriminant accuracy of every two species as the distance between these two points (species). This allows us to conduct a cluster analysis for all samples of the 15 species by the method of hierarchical clustering [37]. The result is given in Figure 10(b), which suggests that the 15 tree species’ leaf samples can be clustered into five groups: (i) {Ulmus carpinifolia, Salix aurita, Ulmus glabra, Salix sinerea, Fagus silvatica}; (ii) {Betula pubescens, Populus, Sorbus intermedia}; (iii) {Quercus, Alnus incana, Salix alba Sericea, Populus tremula}; (iv) {Acer, Tilia} and (v) {Sorbus aucuparia}. This is consistent with visualizing the images directly from Figure 1 showing our proposed approach is applicable.Figure 10

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