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62 lines
2.2 KiB
Plaintext
Executable File
62 lines
2.2 KiB
Plaintext
Executable File
03/12/2012
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1. One-hundred plant species leaves data set.
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2. 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 in this submission.
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The Leaves were collected in the Royal Botanic Gardens, Kew, UK.
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email: james.cope@kingston.ac.uk
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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 database Charles Mallah: charles.mallah@kingston.ac.uk; James Cope: james.cope@kingston.ac.uk
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(c) Date received 03/12/2012
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3. Past Usage:
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(a) This is a new data set, provisional paper:
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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.
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(b) Previous parts of the data set relate to feature extraction of leaves from:
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J. Cope, P. Remagnino, S. Barman, and P. Wilkin.
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Plant texture classification using gabor cooccurrences.
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Advances in Visual Computing,
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pages 669–677, 2010.
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T. Beghin, J. Cope, P. Remagnino, and S. Barman.
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Shape and texture based plant leaf classification. In
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Advanced Concepts for Intelligent Vision Systems,
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pages 345–353. Springer, 2010.
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4. Relevant Information Paragraph:
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The data directory contains the binary images (masks) of the leaf samples. The colour images are not included.
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The data set features are organised as the following:
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'data_Sha_64.txt'
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'data_Tex_64.txt'
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'data_Mar_64.txt'
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One file for each 64-element feature vectors. Each row begins with the class label.
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The remaining 64 elements is the feature vector.
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5. Number of Instances
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1600 samples each of three features (16 samples per leaf class).
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6. Number of Attributes
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Three 64 element feature vectors per sample.
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7. Vectors
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There are three features: Shape, Margin and Texture. As discussed in the paper(s) above.
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For Each feature, a 64 element vector is given per sample of leaf.
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These vectors are taken as a contigous descriptors (for shape) or histograms (for texture and margin).
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8. Missing Attribute Values: none
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9. Class Distribution: 16 instances per class |