基于主题树模型的主题距离及其分类算法

本文介绍了基于向量空间的常用距离的算法，并在概念语义空间的基础上，提出一种面向主题的距离和分类的算法。实验结果表明，该算法能有效地提高主题分类的准确度，可用于主题检索、搜索和聚类等方面。     

Optimal classification methods for mapping agricultural tillage practices

The classification of agricultural tillage systems has proven challenging in the past using traditional classification methods due to the similarity of spectral reflectance signatures of soils and senescent crop residues. In this study, five classification methods were examined to determine the most suitable classification algorithm for the identification of no-till (NT) and traditional tillage (TT) cropping methods: minimum distance (MD), Mahalanobis distance, Maximum Likelihood (ML), spectral angle mapping (SAM), and the cosine of the angle concept (CAC). A Landsat ETM+ image acquired over southern Michigan and northern Indiana was used to test these classification methods. Each classification method was validated with 293 ground truth sampling locations collected commensurate with the satellite overpass. Classification accuracy was then assessed using error matrix analysis, Kappa statistics, and tests for statistical significance. The results indicate that of the classification routines examined, the two spectral angle methods were superior to the others. The cosine of the angle concept algorithm outperformed all the other classification routines for tillage practice identification and mapping, yielding an overall accuracy of 97.2% (Kappa=0.959).     

Embodied categorisation for vision-guided mobile robots

This paper outlines a philosophical and psycho-physiological basis for embodied perception, and develops a framework for conceptual embodiment of vision-guided robots. We argue that categorisation is important in all stages of robot vision. Further, that classical computer vision is unsuitable for this categorisation, however, through conceptual embodiment, active perception can be effective. We present a methodology for developing vision-guided robots that applies embodiment, explicitly and implicitly, in categorising visual data to facilitate efficient perception and action. Finally, we present systems developed using this methodology, and demonstrate that embodied categorisation can make algorithms more efficient and robust.     

基于像元与对象分类的景观指数差异性分析

景观指数分析是进行遥感监测土地利用及变化空间格局的重要方法之一,而因为遥感分类方法的不同,所计算出的同一景观指数所揭示的土地变化模式可能出现较大差异。以北京市密云县为研究区,对影像分别进行了面向对象分类与基于像元的监督分类,并对两种分类进行景观指数计算,利用配对样本t检验方法对各景观指数进行了差异显著性分析。分析结果显示地类面积比例(PLAND)和最大斑块指数(LPI)在两种分类方法之间差异不显著;边缘密度(ED)略有显著差异性;而斑块数量(NP)、斑块面积平均值(AREA_MN)、聚合度(AI)与周长面积分维数(PAFRAC)在两种分类方法之间差异显著。     

Parallelizing AdaBoost by weights dynamics

AdaBoost is one of the most popular classification methods. In contrast to other ensemble methods (e.g., Bagging) the AdaBoost is inherently sequential. In many data intensive real-world situations this may limit the practical applicability of the method. P-AdaBoost is a novel scheme for the parallelization of AdaBoost, which builds upon earlier results concerning the dynamics of AdaBoost weights. P-AdaBoost yields approximations to the standard AdaBoost models that can be easily and efficiently distributed over a network of computing nodes. Properties of P-AdaBoost as a stochastic minimizer of the AdaBoost cost functional are discussed. Experiments are reported on both synthetic and benchmark data sets.     

基于新型不纯度度量的代价敏感随机森林分类器

针对不平衡数据集的有效分类问题,提出一种结合代价敏感学习和随机森林算法的分类器。首先提出了一种新型不纯度度量,该度量不仅考虑了决策树的总代价,还考虑了同一节点对于不同样本的代价差异;其次,执行随机森林算法,对数据集作K次抽样,构建K个基础分类器;然后,基于提出的不纯度度量,通过分类回归树(CART)算法来构建决策树,从而形成决策树森林;最后,随机森林通过投票机制做出数据分类决策。在UCI数据库上进行实验,与传统随机森林和现有的代价敏感随机森林分类器相比,该分类器在分类精度、AUC面积和Kappa系数这3种性能度量上都具有良好的表现。     

地物波谱数据库应用方法及遥感应用现状

地物波谱数据库在遥感信息提取中具有重要的应用价值,本文归纳和总结了常用的国内外通用型地物波谱数据库与专业型地物波谱数据库的发展现状。在对有关波谱库遥感应用文献进行计量分析的基础上,综述了地物波谱数据库遥感应用的四种主要方法波谱特征分析、光谱匹配识别、混合像元分解以及参数提取建模,阐述了地物波谱数据库在地物分类、目标识别及参数反演中的应用。从当前所处的遥感"大数据"时代背景出发,亦对地物波谱数据库的建设趋势与应用潜力进行了展望。     

k-best MIRA和动态k-best MIRA

MIRA(Margin Infused Relaxed Algorithm)是一种超保守算法,在分类、排序、回归等应用领域都取得不错成绩.文中在传统MIRA算法基础上进行改进,提出k-best MIRA(K-MIRA)与动态k-best MIRA(DK-MIRA)算法.这两种算法能够根据学习进程自动调整优化约束条件,从而提高算法的收敛速度与性能.将K-MIRA与DK-MIRA用于定义类问题回答中的句子排序任务,取得较为满意的实验结果.     

城市建筑风景照片的自动识别和分类

在数字照片的快速检索和浏览域中，需要对照片进行分类，但目前国内外学者对照片的分类的研究并不是很多。提出了一种如何使用图像的低级特征来表示高级语义分类的一种方法来对家度数字照片的进行分类，特别适合于有人工建筑或人工事物的图片。算法根据图像纹理点和边缘点的方向相位立方图来推断照片中是否存在人工建筑，从而达到对城市建筑风景和其它风景分类的目的。实验数据表明，分类效果非常好，算法己在Windows2000下使用VC实现，具有很强的鲁棒性。     

综合优度法和不一致性法的最优分割参数选择方法

分割参数的选择直接决定着影像对象的大小、形状。因此如何选择最优的分割参数显得尤为重要。用局部标准差和局部Moran指数构建了新的优度度量函数,然后与通过不一致性法选择的分割参数进行比较,进而选取综合的最优分割参数。通过对高空间分辨率IKONOS影像上农田、草地、池塘和建筑物等4种不同类型地物进行实验,表明不同地物类型具有不同的最优分割参数区间,且分别使用本研究所提出的优度法和不一致性法所得度量参数具有基本一致的最优参数分布区间。进而,通过对最优分割参数和其他分割参数下的分割结果进行影像分类,分类结果的精度评价表明综合两种度量方法得到的最优分割参数可获得最佳的分类结果。