一种新的快速特征选择和数据分类方法

针对数据分类问题提出一种新型高效的特征选择和规则提取方法.首先通过减少初始区间数量改进Chi-Merge离散化方法,再采用改进的Chi-Merge离散化连续型特征变量;特征离散化后,统计样本数据在每个特征子集划分下的频数表,并根据频数表计算数据不一致率,再利用顺序前向最优搜索的方法,快速确定特征数量由小到大的每一个最优特征子集;根据特征子集对应的数据不一致率差异最小化原则,完成特征个数最小化的最优特征子集筛选;根据最优特征子集的数据频数表,可直接提取数据分类规则.实验表明,快速提取的规则可获得较好的分类效果.基于该特征选择方法,提出一种面向分布式同构数据的快速分类模型,不但具有良好的分类效果,还支持对样本数据内容的隐私保护.     

融合纹理和形状特征的高分辨率遥感影像道路提取

提出一种结合纹理和形状特征提取道路信息的方法。首先利用灰度共生矩阵提取纹理特征,并将其应用于最大似然分类中提取面状道路,然后利用形态学方法分割道路与其相连地物,最后利用提出的3个形状指数(凹度、精密度、偏心角)有效地识别和区分了道路与非道路地物,并最终实现了提纯道路的目的。实验结果证明,该方法可以准确地提取主干道路网,剔除非道路地物的影响。     

An efficient orientation distance–based discriminative feature extraction method for multi-classification

Feature extraction is an important step before actual learning. Although many feature extraction methods have been proposed for clustering, classification and regression, very limited work has been done on multi-class classification problems. This paper proposes a novel feature extraction method, called orientation distance–based discriminative (ODD) feature extraction, particularly designed for multi-class classification problems. Our proposed method works in two steps. In the first step, we extend the Fisher Discriminant idea to determine an appropriate kernel function and map the input data with all classes into a feature space where the classes of the data are well separated. In the second step, we put forward two variants of ODD features, i.e., one-vs-all-based ODD and one-vs-one-based ODD features. We first construct hyper-plane (SVM) based on one-vs-all scheme or one-vs-one scheme in the feature space; we then extract one-vs-all-based or one-vs-one-based ODD features between a sample and each hyper-plane. These newly extracted ODD features are treated as the representative features and are thereafter used in the subsequent classification phase. Extensive experiments have been conducted to investigate the performance of one-vs-all-based and one-vs-one-based ODD features for multi-class classification. The statistical results show that the classification accuracy based on ODD features outperforms that of the state-of-the-art feature extraction methods.     

B-Rep model simplification by automatic fillet/round suppressing for efficient automatic feature recognition

The CAD models of real-world mechanical parts usually have many fillets and rounds that are essentially important to ensure the manufacturability and assembability. In feature-based modeling, fillets and rounds are often referred as secondary features that are used to modify the local details of the primary features such as holes, slots and pockets. Although the major shape of the primary features may not be affected, fillets and rounds can greatly change the geometric and topological patterns of the primary features. The geometric and topological variations can result in inefficient feature semantics classification in feature recognition. When feature interactions occur, it may become even worse to identify the regular patterns of the primary features. In addition, the fillets and rounds consist of no-linear surfaces such as cylindrical surfaces, spherical surfaces and toroidal surfaces, which bring the difficulties in volumetric feature extraction by half-space partition. In order to facilitate volumetric feature extraction and feature semantics classification, we pre-process the input B-Rep models by suppressing fillets and rounds before the feature recognition. Thus the input B-Rep models can be simplified without altering the major shapes of primary features, the targets of the feature recognition. The B-Rep simplification can be viewed as the reverse process of the edge blending in feature-based design. In this paper, several issues on fillet/round suppressing are discussed and a relatively general and robust approach is proposed to suppress blendings in B-Rep models of mechanical parts before the surface feature recognition and volumetric feature extraction.     

基于特征通道和空间位置注意力的三维点云特征学习网络

点云模型的分类与部件分割是三维点云数据处理的基本任务,其核心在于获取可以有效表示三维模型的点云特征。提出一个引入注意力机制的三维点云特征学习网络。该网络采用多层次点云特征提取方法,首先使用特征通道注意力模块获取各通道间的关联,增强关键通道信息; 接着引入空间位置注意力机制,基于点的空间位置信息获取各点的注意力权重;然后结合以上2种注意力机制获取增强的点云特征;最后基于该特征继续进行多层次特征提取,获得面向下游任务的点云特征。分别在ModelNet40和ShapeNet数据集上进行形状分类与部件分割实验,结果表明,使用所提方法可以实现高精度、具有鲁棒性的三维点云形状分类与分割。     

自动视频分类中的数据预处理

提出了一套视频数据采集、特征提取及数据预处理方法。介绍了视频数据的采集方式，并提出了一套可计算的视频特征属性，可以反映人们对不同的视频风格的典型特征的理解。给出了如何将这些提取的视频属性数据处理成适合于决策树分类、分类关联规则以及序列模式等数据挖掘技术适用的数据格式。     

Content-based image retrieval methods

Creation of a content-based image retrieval system implies solving a number of difficult problems, including analysis of low-level image features and construction of feature vectors, multidimensional indexing, design of user interface, and data visualization. Quality of a retrieval system depends, first of all, on the feature vectors used, which describe image content. The paper presents a survey of common feature extraction and representation techniques and metrics of the corresponding feature spaces. Color, texture, and shape features are considered. A detailed classification of the currently known features’ representations is given. Experimental results on efficiency comparison of various methods for representing and comparing image content as applied to the retrieval and classification tasks are presented.     

特征识别中的不确定性推理算法研究

介绍了一种智能特征识别器，它能利用零部件已有的几何、公差、属性以及设计特征等信息，自动生成相应的特征模型解析，特征识别机利用了一种基于线索的方法，并采纳了人工智能的技术，如黑板结构及不确定性推理方法，以达到几何完整性的推导。     

False positive reduction in urinary particle recognition

The identification of non-cell objects in biological images is not a trivial task largely due to the difficulty in describing their characteristics in recognition systems. In order to better reduce the false positive rate caused by the presence of non-cell particles, we propose a novel approach using a local jet context features scheme combined with a two-tier object classification system. The newly proposed feature scheme, namely local jet context feature, integrates part of global features with the “local jet” features. The scheme aims to effectively describe the particle characteristics that are invariant to shift and rotation, and hence help to retain the critical shape information. The proposed two-tier particle classification strategy consists of a pre-recognition stage first and later a further filtering phase. Using the local jet context features coupled with a multi-class SVM classifier, the pre-recognition stage intends to assign the particles to their corresponding classes as many as possible. To further reduce the false positive particles, next a decision tree classifier based on shape-centered features is applied. Our experimental study shows that through the proposed two-tier classification strategy, we are able to achieve 85% of identification accuracy and 80% of F₁ value in urinary particle recognition. The experiment results demonstrate that the proposed local jet context features are capable to discriminate particles in terms of shape and texture characteristics. Overall, the two-tier classification stage is found to be effective in reducing the false positive rate caused by non-cell particles.     

基于决策树的面向对象变化信息自动提取研究

为了从不同时相的遥感影像数据中自动提取变化信息且保证其效率,本文结合面向对象分析技术,提出了一种基于决策树变化信息自动提取的新方法。该方法利用影像的特征指数及形状特征、光谱特征、纹理特征等作为特征集,将其作为知识库应用到决策树控制模型中,进而利用该模型实现自动分类。对所得到的分类后影像对象,组织分析其综合属性并作为决策规则再次分类,通过"双重分类"的方式实现面向对象的遥感影像变化信息自动提取。该方法为遥感影像变化信息自动提取提供了新的思路。     

IC真实缺陷的边界提取和缺陷尺寸与形状的表征

为了对ＩＣ制造中真实多余物缺陷进行分类与识别,ＩＣ多余物缺陷的特征提取是非常重要的一步,文中首先提出一种基于数学形态学的ＩＣ真实多余物缺陷边界的检测方法,其次对边界进行了链码描述,最后对边界所表示的多余物缺陷进行了尺寸测量与形状分析,在预处理阶段,利用彩色ＨＳＶ模型分割原ＩＣ图像,然后用图形学开运算消除背景噪音,对开后的结果图像进行形态膨胀及形态腐蚀运算,消除多余物缺陷中的小洞噪音以获得从复杂背景     

Cork quality classification system using a unified image processingand fuzzy-neural network methodology

Cork is a natural material produced in the Mediterranean countries. Cork stoppers are used to seal wine bottles, Cork stopper quality classification is a practical pattern classification problem. The cork stoppers are grouped into eight classes according to the degree of defects on the cork surface. These defects appear in the form of random-shaped holes, cracks, and others. As a result, the classification cork stopper is not a simple object recognition problem. This is because the pattern features are not specifically defined to a particular shape or size. Thus, a complex classification form is involved, Furthermore, there is a need to build a standard quality control system in order to reduce the classification problems in the cork stopper industry. The solution requires factory automation meeting low time and reduced cost requirements. This paper describes a cork stopper quality classification system using morphological filtering and contour extraction and following (CEF) as the feature extraction method, and a fuzzy-neural network as a classifier. This approach will be used on a daily basis. A new adaptive image thresholding method using iterative and localized scheme is also proposed, A fully functioning prototype of the system has been built and successfully tested. The test results showed a 6.7% rejection ratio, It is compared with the 40% counterpart provided by traditional systems. The human experts in the cork stopper industry rated this proposed classification approach as excellent.     

中文文本分类中基于概念屏蔽层的特征提取方法

本文提出了一种新的基于概念抽取和屏蔽层的特征选择方法。该方法利用HowNet概念词典中的概念树,通过义原在概念树中的位置信息进行概念抽取,并赋予其适当权值来说明其描述能力。对于权值低于屏蔽层的义原,我们不将其选入特征集,并相应保留原词。具体到每个词,我们计算其DEF条目中的权值,决定是将原词选入特征集还是进行概念抽取。本文重点研究了如何给义原设定一个合适的权值,如何在选取原词和概念之间取得平衡以及针对非概念词的加权处理。实验证明,设定合适的屏蔽层,不仅可以缩小特征维数,使分类正确率得到一定的提高,而且可以减少不同类别间的分类正确率的差别。     

基于实体模型的产品形状特征识别

依据实体造型的特点,从模型空间的特征层入手,搜寻模型空间中的简单形状特征,并根据造型特点获取单个特征在零件模型上的几何拓扑关系,从而实现形状特征的自动识别.通过分析特征实体造型,提出一种基于实体模型的产品形状特征识别方法,并结合微波器件产品开发特征参数提取模块,实现复杂零件模型形状特征的自动识别与提取.该方法为快速建立企业零件库提供途径,也为网络环境下的协同设计和数据共享奠定基础.     

Biological shape analysis by digital curvature

This paper reports the novel application of digital curvature as a feature for morphological characterization and classification of landmark shapes. By inheriting several unique features of the continuous curvature, the digital curvature provides invariance to translations, rotations, local shape deformations, and is easily made tolerant to scaling. In addition, the bending energy, a global shape feature, can be directly estimated from the curvature values. The application of these features to analyse patterns of cranial morphological geographic differentiation in the rodent species Thrichomys apereoides has led to encouraging results, indicating a close correspondence between the geographical and morphological distributions.     

Facial feature localization based on an improved active shape model

The original active shape model (ASM) has already been applied to the areas such as image segmentation, feature points localization, and contour extraction. However, the original ASM suffers from the loss of accuracy and low speed in real time applications. Due to this, a new scheme of active shape model for facial feature extraction is proposed in this paper. In this scheme, the improvement of the performance of the original ASM concerns the following three aspects. Firstly, the profile of the original ASM is extended from 1D to 2D. Secondly, each profile related to different features are constructed separately. Thirdly, the length of the profile varies with different levels. The simulations are carried out using the SJTU dataset, which contains 2273 face images. Experimental results demonstrate that the proposed scheme exhibits better performance than the original ASM.     

Feature Generation Using General Constructor Functions

Most classification algorithms receive as input a set of attributes of the classified objects. In many cases, however, the supplied set of attributes is not sufficient for creating an accurate, succinct and comprehensible representation of the target concept. To overcome this problem, researchers have proposed algorithms for automatic construction of features. The majority of these algorithms use a limited predefined set of operators for building new features. In this paper we propose a generalized and flexible framework that is capable of generating features from any given set of constructor functions. These can be domain-independent functions such as arithmetic and logic operators, or domain-dependent operators that rely on partial knowledge on the part of the user. The paper describes an algorithm which receives as input a set of classified objects, a set of attributes, and a specification for a set of constructor functions that contains their domains, ranges and properties. The algorithm produces as output a set of generated features that can be used by standard concept learners to create improved classifiers. The algorithm maintains a set of its best generated features and improves this set iteratively. During each iteration, the algorithm performs a beam search over its defined feature space and constructs new features by applying constructor functions to the members of its current feature set. The search is guided by general heuristic measures that are not confined to a specific feature representation. The algorithm was applied to a variety of classification problems and was able to generate features that were strongly related to the underlying target concepts. These features also significantly improved the accuracy achieved by standard concept learners, for a variety of classification problems.     

Automatic Feature Extraction for Classifying Audio Data

Today, many private households as well as broadcasting or film companies own large collections of digital music plays. These are time series that differ from, e.g., weather reports or stocks market data. The task is normally that of classification, not prediction of the next value or recognizing a shape or motif. New methods for extracting features that allow to classify audio data have been developed. However, the development of appropriate feature extraction methods is a tedious effort, particularly because every new classification task requires tailoring the feature set anew.This paper presents a unifying framework for feature extraction from value series. Operators of this framework can be combined to feature extraction methods automatically, using a genetic programming approach. The construction of features is guided by the performance of the learning classifier which uses the features. Our approach to automatic feature extraction requires a balance between the completeness of the methods on one side and the tractability of searching for appropriate methods on the other side. In this paper, some theoretical considerations illustrate the trade-off. After the feature extraction, a second process learns a classifier from the transformed data. The practical use of the methods is shown by two types of experiments: classification of genres and classification according to user preferences.