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Update README.md
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README.md
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tags:
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- biology
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size_categories:
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---
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Description:
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One-hundred plant species leaves dataset
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The dataset is derived from this paper: Charles Mallah, James Cope, James Orwell. Plant Leaf Classification Using Probabilistic Integration of Shape, Texture and Margin Features. Signal Processing, Pattern Recognition and Applications, in press. 2013.
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Sources:
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(a) Original owners of colour Leaves Samples:
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James Cope, Thibaut Beghin, Paolo Remagnino, Sarah Barman.
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The colour images are not included.
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(b) This dataset consists of work carried out by James Cope, Charles Mallah, and James Orwell.
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Donor of the database:
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Charles Mallah, charles.mallah@kingston.ac.uk; James Cope, james.cope@kingston.ac.uk.
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The original data directory contains the binary images (masks) of the leaf samples (colour images not included). There are three features for each image: Shape, Margin and Texture. For each feature, a 64 element vector is given per leaf sample. These vectors are taken as a contiguous descriptor (for shape) or histograms (for texture and margin). So, there are three different files, one for each feature problem:
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‘data_Sha_64.txt’ -> prediction based on shape
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‘data_Tex_64.txt’ -> prediction based on texture
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‘data_Mar_64.txt’ -> prediction based on margin
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Each row has a 64-element feature vector followed by the Class label. There is a total of 1600 samples with 16 samples per leaf class (100 classes), and no missing values.
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tags:
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- biology
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size_categories:
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- 10M<n<100M
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---
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Description:
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One-hundred plant species leaves dataset
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The dataset is derived from this paper: Charles Mallah, James Cope, James Orwell. Plant Leaf Classification Using Probabilistic Integration of Shape, Texture and Margin Features. Signal Processing, Pattern Recognition and Applications, in press. 2013.
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Sources:
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+
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(a) Original owners of colour Leaves Samples:
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James Cope, Thibaut Beghin, Paolo Remagnino, Sarah Barman.
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The colour images are not included.
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(b) This dataset consists of work carried out by James Cope, Charles Mallah, and James Orwell.
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Donor of the database:
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Charles Mallah, charles.mallah@kingston.ac.uk; James Cope, james.cope@kingston.ac.uk.
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The original data directory contains the binary images (masks) of the leaf samples (colour images not included). There are three features for each image: Shape, Margin and Texture. For each feature, a 64 element vector is given per leaf sample. These vectors are taken as a contiguous descriptor (for shape) or histograms (for texture and margin). So, there are three different files, one for each feature problem:
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‘data_Sha_64.txt’ -> prediction based on shape
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+
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‘data_Tex_64.txt’ -> prediction based on texture
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+
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‘data_Mar_64.txt’ -> prediction based on margin
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Each row has a 64-element feature vector followed by the Class label. There is a total of 1600 samples with 16 samples per leaf class (100 classes), and no missing values.
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