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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 flow chart of software programing base on our model is as follows. Detailed codes are available upon request.
© Copyright Policy - open-access
Related In: Results  -  Collection

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Fig14: The flow chart of software programing base on our model is as follows. Detailed codes are available upon request.

Mentions: In this paper we have adopted the 2D MF-DFA method proposed in [32] to extract important texture features from leaf images. This allow us to calculate the generalized Hurst exponents, h(q), and several other multifractal parameters including αmax, αmin, ∆α, ∆f, D1 and D2. By defining an index, I0, which examines the variation of the inter-species variances and the intra-species variances, we are able to find an optimal combination of the multifractal parameters that best characterizes the key features of plant species allowing high accuracy in plant species identification. For the Swedish leaf data set which contains 15 species and 75 × 15 = 1125 samples in total [31], the combination of {h(−3), αmin, Δα} turns out to be optimal compared to other combinations of parameters. We have obtained 98.4% of averaged discriminant accuracy for every two species by SVMKM with the 10 − fold cross validation, while the accuracy reaches 93.96% for the over-all 15 species. Software based on our work can be designed and coded, for that purpose, we provided the corresponding flow chart in the Figure 14.Figure 14


Two-dimensional multifractal detrended fluctuation analysis for plant identification.

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

The flow chart of software programing base on our model is as follows. Detailed codes are available upon request.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig14: The flow chart of software programing base on our model is as follows. Detailed codes are available upon request.
Mentions: In this paper we have adopted the 2D MF-DFA method proposed in [32] to extract important texture features from leaf images. This allow us to calculate the generalized Hurst exponents, h(q), and several other multifractal parameters including αmax, αmin, ∆α, ∆f, D1 and D2. By defining an index, I0, which examines the variation of the inter-species variances and the intra-species variances, we are able to find an optimal combination of the multifractal parameters that best characterizes the key features of plant species allowing high accuracy in plant species identification. For the Swedish leaf data set which contains 15 species and 75 × 15 = 1125 samples in total [31], the combination of {h(−3), αmin, Δα} turns out to be optimal compared to other combinations of parameters. We have obtained 98.4% of averaged discriminant accuracy for every two species by SVMKM with the 10 − fold cross validation, while the accuracy reaches 93.96% for the over-all 15 species. Software based on our work can be designed and coded, for that purpose, we provided the corresponding flow chart in the Figure 14.Figure 14

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