100种植物叶片数据集

Data Set Information: For Each feature, a 64 element vector is given per sample of leaf. These vectors are taken as a contigous descriptors (for shape) or histograms (for texture and margin). Attribute Information: For Each feature, a 64 element vector is given per sample of leaf. One file for each 64-element feature vectors. Each row begins with the class label. The remaining 64 elements is the feature vector. Relevant Papers: This is a new data set, provisional paper: 'Plant Leaf Classification Using Probabilistic Integration of Shape, Texture and Margin Features' at SPPRA 2013. Authors: Charles Mallah, James Cope, and James Orwell or Kingston University London. Previous parts of the data set relate to feature extraction of leaves from: J. Cope, P. Remagnino, S. Barman, and P. Wilkin. Plant texture classification using gabor cooccurrences. Advances in Visual Computing, pages 669a€“677, 2010. T. Beghin, J. Cope, P. Remagnino, and S. Barman. Shape and texture based plant leaf classification. In Advanced Concepts for Intelligent Vision Systems, pages 345a€“353. Springer, 2010. Citation Request: 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. James Cope, Thibaut Beghin, Paolo Remagnino, Sarah Barman. The colour images are not included in this submission. The Leaves were collected in the Royal Botanic Gardens, Kew, UK. email: james.cope '@' kingston.ac.uk This dataset consists of work carried out by James Cope, Charles Mallah, and James Orwell. Kingston University London. Donor of database Charles Mallah: charles.mallah '@' kingston.ac.uk; James Cope: james.cope '@' kingston.ac.uk

数据集信息：针对每个特征，每个叶片样本均对应一个64维向量。此类向量可作为形状特征的连续描述符，或纹理与叶缘特征的直方图。
属性信息：针对每个特征，每个叶片样本均对应一个64维向量。每个64维特征向量对应一个单独文件。每行数据以类别标签起始，后续64个元素即为该样本的特征向量。
相关论文：本数据集为全新数据集，配套临时论文为《基于形状、纹理与叶缘特征概率融合的植物叶片分类（Plant Leaf Classification Using Probabilistic Integration of Shape, Texture and Margin Features）》，发表于2013年SPPRA会议。作者为Charles Mallah、James Cope与James Orwell，隶属伦敦金士顿大学（Kingston University London）。
本数据集的前期特征提取工作源自以下文献：
1. J. Cope、P. Remagnino、S. Barman与P. Wilkin. 基于加博尔（Gabor）共生矩阵的植物纹理分类[C]//《视觉计算进展》. 2010: 669–677.
2. T. Beghin、J. Cope、P. Remagnino与S. Barman. 基于形状与纹理的植物叶片分类[C]//《智能视觉系统高级概念》. 2010: 345–353. Springer出版社.
引用要求：
1. Charles Mallah, James Cope, James Orwell. 基于形状、纹理与叶缘特征概率融合的植物叶片分类[J]. 《信号处理、模式识别与应用》, 已录用, 2013.
2. James Cope, Thibaut Beghin, Paolo Remagnino, Sarah Barman. 本次提交未包含彩色图像数据。
本数据集的叶片样本采集自英国皇家植物园邱园（Royal Botanic Gardens, Kew, UK）。
联系邮箱：[e-mail]
本数据集由伦敦金士顿大学的James Cope、Charles Mallah与James Orwell完成相关研究工作。
数据集提供者：Charles Mallah，邮箱：[e-mail]；James Cope，邮箱：[e-mail]

搜集汇总

背景与挑战

背景概述

该数据集包含100种植物叶片的特征数据，每个叶片样本提供64维的特征向量，涵盖形状、纹理和边缘特征。数据集由Kingston University London的研究团队发布，并附有相关研究论文。