A symmetry-based coarse classification method for Chinese characters

In this paper, we present a novel symmetry-based coarse classification method for the preclassification of printed Chinese characters. The proposed method consists of two main modules, recursive radical extraction, and a symmetry test. The former classifies Chinese characters into ten classes according to the composing structure of the characters. Two classes in the ten classes, left-right, and up-down type characters, contain over 85% of the total characters. The latter performs the symmetry test to determine whether the character, or radical in the ten classes, is symmetric or not. The main purpose of the proposed symmetry-test coarse classification method is to reduce the number of characters in each of the ten classes. Four symmetry features are devised to perform the symmetry test. Experimental results reveal that the proposed method can greatly reduce the number of characters in each class to achieve the coarse classification goal.     

Some properties of a classification system for multivariate lifedistributions

A good classification system for multivariate life distributions can play a major role in system analysis during the early stage of product design. The problem of multivariate classification has drawn the attention of the researchers, and various classes are now available in the literature. D. Roy (1994) proposed a unified classification system for the multivariate life laws. It not only retains the much-desired chain of implication but generalizes quite a few characterization results relating to various life distribution classes. This paper examines this classification system of Roy and establishes its importance by presenting some fundamental properties of the same for use during early stages of product planning. These properties are aimed at retaining the same classification results under deletion, addition, and scaling of components and subsystems of a product during the early stages of design. A multivariate series combination has been examined from the viewpoint of closure under multivariate IFR, IFRA, and NBU classes. The major use of a classification system is to provide reliability bounds under breaking the product into meaningful s-independent subsystems and their reintegration. This paper ensures that this happens in the multivariate setup so that close reliability bounds can be obtained in place of a complicated analysis. A demonstration example is presented which describes the calculation of reliability bounds in a general way, covering both series and parallel combinations and a complex dependent setup     

Spectral fuzzy classification: an application

Geographical information (including remotely sensed data) is usually imprecise, meaning that the boundaries between different phenomena are fuzzy. In fact, many classes in nature show internal gradual differences in species, health, age, moisture, as well other factors. If our classification model does not acknowledge that those classes are heterogeneous, and crisp classes are artificially imposed, a final careful analysis should always search for the consequences of such an unrealistic assumption. We consider the unsupervised algorithm presented by A. del Amo et al. (2000), and its application to a real image in Sevilla province (south Spain). Results are compared with those obtained from the ERDAS ISO-DATA classification program on the same image, showing the accuracy of our fuzzy approach. As a conclusion, it is pointed out that whenever real classes are natural fuzzy classes, with gradual transition between classes, then its fuzzy representation will be more easily understood, and therefore accepted by users     

Application of neural networks to radar image classification

A number of methods have been developed to classify ground terrain types from fully polarimetric synthetic aperture radar (SAR) images, and these techniques are often grouped into supervised and unsupervised approaches. Supervised methods have yielded higher accuracy than unsupervised techniques, but suffer from the need for human interaction to determine classes and training regions. In contrast, unsupervised methods determine classes automatically, but generally show limited ability to accurately divide terrain into natural classes. In this paper, a new terrain classification technique is introduced to determine terrain classes in polarimetric SAR images, utilizing unsupervised neural networks to provide automatic classification, and employing an iterative algorithm to improve the performance. Several types of unsupervised neural networks are first applied to the classification of SAR images, and the results are compared to those of more conventional unsupervised methods. Results show that one neural network method-Learning Vector Quantization (LVQ)-outperforms the conventional unsupervised classifiers, but is still inferior to supervised methods. To overcome this poor accuracy, an iterative algorithm is proposed where the SAR image is reclassified using a maximum likelihood (ML) classifier. It is shown that this algorithm converges, and significantly improves classification accuracy     

特征的支持度与其分类能力的关系研究

频繁模式挖掘在分类问题中得到了广泛的应用,大量的工作利用频繁模式挖掘对分类问题进行特征选择,但对于为什么频繁模式挖掘可以在分类问题中进行有效的特征选择则缺乏系统的研究.为了为频繁模式挖掘在分类问题中的特征选择应用提供理论基础,需要确立特征的支持度与特征分类能力之间的关系,本文以特征的信息增益作为分类能力的评价准则,讨论其与特征支持度之间的联系.首先证明了信息增益是特征支持度的上凸函数;然后,在二类问题和多类问题情况下,分别证明了具有低支持度或高支持度的特征具有有限的信息增益,即具有低支持度或高支持度的特征具有有限的分类能力.最后,通过仿真实验验证了支持度与信息增益之间的关系,为频繁模式挖掘在分类问题中的应用提供了理论基础.     

Projection pursuit classification of multiband polarimetric SARland images

Results are presented for an experiment utilizing a pastoral land scene with a variety of eight classes, imaged by the NRL dual band (X and L) polarimetric synthetic aperture radar (NUWSAR) at a spatial resolution of 1.2 m. Projection pursuit (PP) statistical analysis tools were applied to a set of simultaneous L-band and X-band fully polarized images (six independent channels) to demonstrate the utility of land classification at high spatial resolution from a light aircraft using SAR. The statistical confusion matrix was used as a quantitative optimization measure of classification. Samples of eight classes from a portion of the scene were used to define a training set, then PP tools were used for classification. It is clear that L-band and X-band fully polarized data view the classes in a significantly different manner, and each brings independent information to the analysis. These results are not meant to be exhaustive at this time but to demonstrate the utility of applying PP tools to multiband and polarization SAR data and to give an indication of the quality of classification one can achieve with moderately high spatial resolution SAR data using a light plane platform     

Fast likelihood classification

A multistage classification that reduces the processing time substantially is proposed. This classification algorithm consists of several stages, and in each stage likelihood values of classes are calculated and compared. If a class has a likelihood value less than a threshold, the class is truncated at that stage as an unlikely class, thus reducing the number of classes for which likelihood values are to be calculated at the next stage. Thus a host of classes can be truncated by using a small portion of the total features at early stages, resulting in substantial reduction of computing time. Several truncation criteria are developed, and the relationship between thresholds and the error caused by the truncation is investigated. Experiments show that the proposed algorithm reduces the processing time by the factor of 3-7, depending on the number of classes and features, while maintaining essentially the same accuracies     

Partially Supervised classification of remote sensing images through SVM-based probability density estimation

A general problem of supervised remotely sensed image classification assumes prior knowledge to be available for all the thematic classes that are present in the considered dataset. However, the ground-truth map representing that prior knowledge usually does not really describe all the land-cover typologies in the image, and the generation of a complete training set often represents a time-consuming, difficult and expensive task. This problem affects the performances of supervised classifiers, which erroneously assign each sample drawn from an unknown class to one of the known classes. In the present paper, a classification strategy is described that allows the identification of samples drawn from unknown classes through the application of a suitable Bayesian decision rule. The proposed approach is based on support vector machines (SVMs) for the estimation of probability density functions and on a recursive procedure to generate prior probability estimates for known and unknown classes. In the experiments, both a synthetic dataset and two real datasets were used.     

Context-based multiscale classification of document images usingwavelet coefficient distributions

In this paper, an algorithm is developed for segmenting document images into four classes: background, photograph, text, and graph. Features used for classification are based on the distribution patterns of wavelet coefficients in high frequency bands. Two important attributes of the algorithm are its multiscale nature-it classifies an image at different resolutions adaptively, enabling accurate classification at class boundaries as well as fast classification overall-and its use of accumulated context information for improving classification accuracy.     

Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images

This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.     

基于位置敏感模型的深度跟踪算法研究

视觉目标跟踪旨在寻找与跟踪目标具有相同语义信息的样本,并在视频中精确定位样本的位置.最近,深度分类模型被用来提取跟踪目标的深度嵌入式特征,然而,由于深度分类模型给予相同类别的样本一样的标签,这样容易导致跟踪模糊,甚至失败.为了解决这个问题,本文将样本的空间位置信息加入深度分类模型中,提出位置敏感损失函数.本文所提出的损失函数不仅继承了分类损失函数的特性,并根据样本的空间位置信息对相同标签的样本进行了排序.也就是说,本文的损失函数可以同时实现类间可分和类内排序.相比于分类损失函数,本文的损失函数更适合目标跟踪任务.本文在OTB100^[1]和VOT2016^[2]上进行了测试,结果表明本文算法可以实现较好的跟踪性能.     

基于目标散射相似性的POLSAR图像无监督 地物散射分类新方案

针对H-Alpha散射分类存在的不足,本文提出了一种基于目标散射相似性的POLSAR图像地物散射分类新方案.该方案首先利用散射随机性将地物分为高散射随机性、中散射随机性和低散射随机性三类,然后根据散射相似性参数对这三类进一步细分.由于该方案根据散射相似性参数自动确定散射类别,克服了采用Alpha人工确定散射类别的不足;散射相似性参数计算简便,克服了H-Alpha散射分类运算量偏大的不足.作为一种实际应用,在新方案框架下,本文给出了一种基于球面散射、偶次散射和体散射相似性参数,以及散射随机性的度量参数——极化散射熵的散射分类新方法.该方法在利用极化散射熵将地物分为三类基础上,根据三个散射相似性参数进一步将地物分为十类.由于这三种典型散射对应实际地物散射,该方法的十种散射类别能很好地描述实际地物情况.实测极化数据的实验结果,验证了新方案的可行性和新方法的有效性.     

基于遗传算法的非监督层次分类方法研究

快速准确地确定单个样本的所属类别以及总体样本类别数是解决非监督模式识别的前提,然而它们的确定通常是非常困难的.通过研究基于遗传算法的相似性度量最优分类算法以及最优分类数确定算法,提高非监督识别的准确性,并将所研究的算法应用到飞机识别当中.实验结果表明,本算法可以进行最优分类及分类数的确定.     

一种特征压缩及分类神经网络的研究

由于多对多类问题的高维数据无法直接观察其聚类和分布特性，本文采用神经网络法实现自适应主元特征提取（ＡＰＥＸ），以压缩特征空间的维数，并保持足够的信息来鉴别事物之间的类型，它可有效地提取信号的主要特征和抑制噪声，我们将高维数据压缩影射到２或３维，从而实现特征数据的可视性分析，显示物体对象间的类似程度和关系结构，并采用高阶函数的神经网络对其进行了非线性分类，同时与ＢＰ网络的非线性分类能力进行了实验比较     

一种基于SVM的遥感影像分类技术

支持向量机（SVM）应用到高光谱图像分类中有较好的识别效果。但用它来分类数据量大、维数高的高光谱图像时,就会遇到如何选择最佳惩罚因子和最优权向量系数的问题。提出一种改进的多类支持向量机分类方法,在OAO-SVM分类结果的基础上进行二次分类,以改善错分样本较多的类别之间的混淆程度。实验表明,二次分类的多类支持向量机方法是有效的。     

WB-KNN for emotion recognition from physiological signals

K-nearest neighbor (KNN) has yielded excellent performance in physiological signals based on emotion recognition. But there are still some issues:the majority vote only by the nearest neighbors is too simple to deal with complex (like skewed) class distribution; features with the same contribution to the similarity will degrade the classification accuracy; samples in boundaries between classes are easily misclassified when k is larger. Therefore, we propose an improved KNN algorithm called WB-KNN, which takes into account the weight (both features and classification) and boundaries between classes. Firstly, a novel weighting method based on the distance and farthest neighbors named WDF is proposed to weight the classification, which improves the voting accuracy by making the nearer neighbors contribute more to the classification and using the farthest neighbors to reduce the weight of non-target class. Secondly, feature weight is introduced into the distance formula, so that the significant features contribute more to the similarity than noisy or irrelevant features. Thirdly, a voting classifier is adopted in order to overcome the weakness of KNN in boundaries between classes by combining different classifiers. Results of WB-KNN algorithm are encouraging compared with the traditional KNN and other classification algorithms on the physiological dataset with a skewed class distribution. Classification accuracy for 29 participants achieves 94.219 2% for the recognition of four emotions.     

Cluster-space representation for hyperspectral data classification

This paper presents a generalization of the hybrid supervised-unsupervised approach to image classification, and an automatic procedure for implementing it with hyperspectral data. Cluster-space representation is introduced in which clustered training data is displayed in a one-dimensional (1-D) cluster-space showing its probability distribution. This representation leads to automatic association of spectral clusters with information classes and the development of a cluster-space classification (CSC). Pixel labeling is undertaken by a combined decision based on its membership of belonging to defined clusters and the clusters' membership of belonging to information classes. The method provides a means of class data separability inspection, visually and quantitatively, regardless of the number of spectral bands used. The class modeling requires only that first degree statistics be estimated; therefore, the number of training samples required can be many fewer than when using Gaussian maximum likelihood (GML) classification. Experiments are presented based on computer generated data and AVIRIS data. The advantages of the method are demonstrated showing improved capacity for data classification     

面向类不平衡网络流量的特征选择算法

针对网络流量分类过程中出现的类不平衡问题,该文提出一种基于加权对称不确定性(WSU)和近似马尔科夫毯(AMB)的特征选择算法。首先,根据类别分布信息,定义了偏向于小类别的特征度量,使得与小类别具有强相关性的特征更容易被选择出来;其次,充分考虑特征与类别间、特征与特征之间的相关性,利用加权对称不确定性和近似马尔科夫毯删除不相关特征及冗余特征;最后,利用基于相关性度量的特征评估函数以及序列搜索算法进一步降低特征维数,确定最优特征子集。实验表明,在保证算法整体分类精确率的前提下,算法能够有效提高小类别的分类性能。     

A Simulation Study of Some Contextual Classification Methods For Remotely Sensed Data

Various methods for contextual classification of multispectral scanner data have been developed during the last 15 years, aiming at increased accuracy in classified images. The methods have for a large part been of four main types: 1) neighborhood-based classification based on stochastic models for the classes over the scene and for the vectors given the classes; 2) simultaneous classification of all pixels, using, e.g., Markov random-field models; 3) relaxation methods that iteratively modify posterior probabilities using information from an increasing neighborhood; and 4) methods using ordinary noncontextual rules based on transformed data. In the present paper a selection of these methods is presented and compared using computer-gented data on different scenes. Spatial autocorrelation is present in the data. Error rates are compared, and an attempt is made to characterize what kind of errors each particular method makes.     

面向类不平衡网络流量的特征选择算法

针对网络流量分类过程中出现的类不平衡问题,该文提出一种基于加权对称不确定性(WSU)和近似马尔科夫毯(AMB)的特征选择算法.首先,根据类别分布信息,定义了偏向于小类别的特征度量,使得与小类别具有强相关性的特征更容易被选择出来;其次,充分考虑特征与类别间、特征与特征之间的相关性,利用加权对称不确定性和近似马尔科夫毯删除...