Color Fractal Descriptors for Adaxial Epidermis Texture Classification

The leaves are an important plant organ and source of information for the traditional plant taxonomy. This study proposes a plant classification approach using the adaxial epidermis tissue, a specific cell layer that covers the leaf. To accomplish this task, we apply a high discriminative color texture analysis method based on the Bouligand-Minkowski fractal dimension. In an experimental comparison, the success rate obtained by our proposed approach (\(96.66\%\)) was the highest among all the methods used, demonstrating that the Bouligand-Minkowski method is very suitable to extract discriminant features from the adaxial epidermis. Thus, this research can significantly contribute with other studies on plant classification by using computer vision.

[1]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Paul F. Whelan,et al.  Experiments in colour texture analysis , 2001, Pattern Recognit. Lett..

[3]  Jarbas Joaci de Mesquita Sá Junior,et al.  Plant leaf identification using Gabor wavelets , 2009 .

[4]  Odemir Martinez Bruno,et al.  A computer vision approach to quantify leaf anatomical plasticity: a case study on Gochnatia polymorpha (Less.) Cabrera , 2013, Ecol. Informatics.

[5]  André Ricardo Backes,et al.  A simplified gravitational model to analyze texture roughness , 2012, Pattern Recognit..

[6]  Jarbas Joaci de Mesquita Sá Junior,et al.  Measuring and analyzing color and texture information in anatomical leaf cross sections: an approach using computer vision to aid plant species identification , 2011 .

[7]  André Ricardo Backes,et al.  Plant Leaf Identification Based on Volumetric Fractal Dimension , 2009, Int. J. Pattern Recognit. Artif. Intell..

[8]  André Ricardo Backes,et al.  Plant Species Identification Using Multi-scale Fractal Dimension Applied to Images of Adaxial Surface Epidermis , 2009, CAIP.

[9]  Jian Fan,et al.  Texture Classification by Wavelet Packet Signatures , 1993, MVA.

[10]  Arnold W. M. Smeulders,et al.  Color texture measurement and segmentation , 2005, Signal Process..

[11]  André Ricardo Backes,et al.  Texture analysis and classification using shortest paths in graphs , 2013, Pattern Recognit. Lett..

[12]  Benoit B. Mandelbrot,et al.  Fractal Geometry of Nature , 1984 .

[13]  O. Bruno,et al.  Leaf shape analysis using the multiscale Minkowski fractal dimension, a new morphometric method: a study with Passiflora (Passifloraceae) , 2005 .

[14]  John A. Richards,et al.  Remote Sensing Digital Image Analysis: An Introduction , 1999 .

[15]  Christoph Palm,et al.  Color texture classification by integrative Co-occurrence matrices , 2004, Pattern Recognit..

[16]  André Ricardo Backes,et al.  Texture analysis and classification using deterministic tourist walk , 2010, Pattern Recognit..

[17]  Francesco Bianconi,et al.  Rotation-invariant colour texture classification through multilayer CCR , 2009, Pattern Recognit. Lett..

[18]  C. Tricot.  Curves and Fractal Dimension , 1994 .

[19]  Lei Zhang,et al.  Image retrieval based on micro-structure descriptor , 2011, Pattern Recognit..

[20]  André Ricardo Backes,et al.  Color texture analysis based on fractal descriptors , 2012, Pattern Recognit..

[21]  R.M. Haralick,et al.  Statistical and structural approaches to texture , 1979, Proceedings of the IEEE.

[22]  B. Everitt,et al.  Applied Multivariate Data Analysis: Everitt/Applied Multivariate Data Analysis , 2001 .

[23]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Maria Petrou,et al.  Histogram ratio features for color texture classification , 2003, Pattern Recognit. Lett..

[25]  L. Macaire,et al.  Haralick feature extraction from LBP images for color texture classification , 2008, 2008 First Workshops on Image Processing Theory, Tools and Applications.