Abstract
BackgroundIn 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, I0, 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.ResultsThe 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.ConclusionsOur method, based on the 2D MF-DFA, provides a feasible and efficient procedure to identify plant species.
Highlights
The increasing interest in biodiversity and biocomplexity, together with the growing availability of digital images and image analysis algorithms, makes plant species identification and classification a topic that has attracted many researchers’ attention
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
We have obtained 98.4% of averaged discriminant accuracy for every two species by support vector machines and kernel methods (SVMKM) with the 10 − fold cross validation, while the accuracy reaches 93.96% for the over-all 15 species
Summary
The increasing interest in biodiversity and biocomplexity, together with the growing availability of digital images and image analysis algorithms, makes plant species identification and classification a topic that has attracted many researchers’ attention. Leaf’s shape, color, vein properties, texture and contours are important features for plant identification. Leaf shapes were used in [4,5,6]; complex veins and contours of leaves were used in [7] and leaf texture was used in [8,9,10,11] for plant species identification. 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. I0, 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
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