A model (in)validation approach to gait classification

This paper addresses the problem of human gait classification from a robust model (in)validation perspective. The main idea is to associate to each class of gaits a nominal model, subject to bounded uncertainty and measurement noise. In this context, the problem of recognizing an activity from a sequence of frames can be formulated as the problem of determining whether this sequence could have been generated by a given (model, uncertainty, and noise) triple. By exploiting interpolation theory, this problem can be recast into a nonconvex optimization. In order to efficiently solve it, we propose two convex relaxations, one deterministic and one stochastic. As we illustrate experimentally, these relaxations achieve over 83 percent and 86 percent success rates, respectively, even in the face of noisy data.     

基于风险事件分类的信息系统评估模型研究

提出了一种基于风险事件分类的信息系统评估模型.模型将信息系统的单个风险事件按底层评估指标进行分类,得到底层指标风险值,然后按照评估指标的层次结构由下往上进行计算,并最终得到信息系统的综合风险值.最后,利用模糊综合评判法,在提出的模型上对一个信息系统进行评估,并得出评估结论.     

基于一种新模糊模型的非线性系统模糊辨识

提出一种基于新的模糊模型和加权递推最小二乘算法 (WRLSA)的非线性系统模糊辨识方法.新型的具有插值能力的模糊系统可以通过学习从输入输出采样数据中提取MISO系统模糊规则,它继承了Sugeno模型及其变化形式的许多优点.采用相应的模糊隶属函数,使得被辨识的模型可用若干局部线性模型来表示,然后利用WRLSA拟合这些线性模型.给出了详细的模糊辨识算法,为了验证该辨识方法的有效性,还给出了对熟知的Box-Jenkins数据的辨识结果.     

Silhouette analysis-based gait recognition for human identification

Human identification at a distance has recently gained growing interest from computer vision researchers. Gait recognition aims essentially to address this problem by identifying people based on the way they walk. In this paper, a simple but efficient gait recognition algorithm using spatial-temporal silhouette analysis is proposed. For each image sequence, a background subtraction algorithm and a simple correspondence procedure are first used to segment and track the moving silhouettes of a walking figure. Then, eigenspace transformation based on principal component analysis (PCA) is applied to time-varying distance signals derived from a sequence of silhouette images to reduce the dimensionality of the input feature space. Supervised pattern classification techniques are finally performed in the lower-dimensional eigenspace for recognition. This method implicitly captures the structural and transitional characteristics of gait. Extensive experimental results on outdoor image sequences demonstrate that the proposed algorithm has an encouraging recognition performance with relatively low computational cost.     

Fuzzy neural network approaches for robotic gait synthesis

In this paper, a learning scheme using a fuzzy controller to generate walking gaits is developed. The learning scheme uses a fuzzy controller combined with a linearized inverse biped model. The controller provides the control signals at each control time instant. The algorithm used to train the controller is "backpropagation through time". The linearized inverse biped model provides the error signals for backpropagation through the controller at control time instants. Given prespecified constraints such as the step length, crossing clearance, and walking speed, the control scheme can generate the gait that satisfies these constraints. Simulation results are reported for a five-link biped robot.     

基于样本分析的图像识别分类模型*

利用基于样本训练的统计学习原理,在分析各类图像样本特征上的差异和相关性的基础上,提取图像共同特征和显著特征参数集合,并加入人为启发式思想,结合先验知识的指导和计算机特征分析结果来制订特征提取规则,应用Dempster-Shafer(DS)理论的思想融合提取的多个特征,形成启发式分类模型.该模型可解决计算机视觉的精确性与人类视觉的模糊性相矛盾的问题,并能有效地区分在某些特征上有差异的相似物.     

基于加速度传感器的步态分类研究

提出了一种基于时域和小波能量的步态分类算法。运用三轴加速度传感器采集走路、上楼、下楼三种步态下的上臂和胯部加速度信号,提取平均值、标准差、百分位数、平均绝对偏差等时域特征和2-6层小波能量特征,构建贝叶斯分类器,对三种步态进行分类。分类结果显示,时域与小波能量结合的分类方法的精度高于仅使用时域特征和仅使用小波能量特征的分类精度。     

中文试题模糊自动分类

试题库是在线测试的基础,中文试题的自动分类技术可以提高试题的检索速度和组卷的准确性.中文试题所包含的关键字较少,关键字出现的频度难以掌握,语法结构相似性较大并固定等,利用试题解析算法将试题分解成关键字库和冗字库,建立关键字矩阵,考虑试题的相似性,引入模糊聚类分析技术,对关键字库进行聚类,得到各个主题的类别,通过计算优化后的隶属度对试题进行分类.     

Fuzzy clustering-based discretization for gene expression classification

This paper presents a novel classification approach that integrates fuzzy class association rules and support vector machines. A fuzzy discretization technique based on fuzzy c-means clustering algorithm is employed to transform the training set, particularly quantitative attributes, to a format appropriate for association rule mining. A hill-climbing procedure is adapted for automatic thresholds adjustment and fuzzy class association rules are mined accordingly. The compatibility between the generated rules and fuzzy patterns is considered to construct a set of feature vectors, which are used to generate a classifier. The reported test results show that compatibility rule-based feature vectors present a highly- qualified source of discrimination knowledge that can substantially impact the prediction power of the final classifier. In order to evaluate the applicability of the proposed method to a variety of domains, it is also utilized for the popular task of gene expression classification. Further, we show how this method provide biologists with an accurate and more understandable classifier model compared to other machine learning techniques.     

An automated hierarchical gait pattern identification tool employing cross-correlation-based feature extraction and recurrent neural network based classification

Abstract: In this paper Elman's recurrent neural network (ERNN) is employed for automatic identification of healthy and pathological gait and subsequent diagnosis of the neurological disorder in pathological gaits from the respective gait patterns. Stance, swing and double support intervals (expressed as percentages of stride) of 63 subjects were analysed for a period of approximately 300 s. The relevant gait features are extracted from cross-correlograms of these signals with corresponding signals of a reference subject. These gait features are used to train modular ERNNs performing binary and tertiary classifications. The average accuracy of binary classifiers is obtained as 90.6%–97.8% and that of tertiary classifiers is 89.8%. Hence, two hierarchical schemes are developed each of which uses more than one modular ERNN to segregate healthy, Parkinson's disease, Huntington's disease and amyotrophic lateral sclerosis subjects. The average testing performances of the schemes are 83.8% and 87.1%.     

Correlation-based classification of heartbeats for individual identification

This paper proposes new techniques to delineate P and T waves efficiently from heartbeats. The delineation results have been found to be optimum and stable in comparison to other published results. These delineators are used along with QRS complex to extract various features of classes time interval, amplitude and angle from clinically dominant fiducials on each heartbeat of the electrocardiogram (ECG). A new identification system has been proposed in this study, which uses these features and makes the decision on the identity of an individual with respect to a given database. The system has been tested against a set of 250 ECG recordings prepared from 50 individuals of Physionet. The matching decisions are made on the basis of correlation between heartbeat features among individuals. The proposed system has achieved an equal error rate of less than 1.01 with an accuracy of 99%.     

Fuzzy sets of rules for system identification

The synthesis of fuzzy systems involves the identification of a structure and its specialization by means of parameter optimization. In doing this, symbolic approaches which encode the structure information in the form of high-level rules allow further manipulation of the system to minimize its complexity, and possibly its implementation cost, while all-parametric methodologies often achieve better approximation performance. In this paper, we rely on the concept of a fuzzy set of rules to tackle the rule induction problem at an intermediate level. An online adaptive algorithm is developed which almost surely learns the extent to which inclusion of a rule in the rule set significantly contributes to the reproduction of the target behavior. Then, the resulting fuzzy set of rules can be defuzzified to give a conventional rule set with similar behavior. Comparisons with high-level and low-level methodologies show that this approach retains the most positive features of both     

Fuzzy beans in classification

In the article fuzzy bean based classifier is given. Three different type of structures are tested in the classifier. Fuzzy bean based classifier is supervised learning method and with proper optimization scheme promising results were covered. Differential evolution algorithm was used in optimizing the required parameters. Classifier was applied to diagnosis of liver-disorders with 73.9% classification accuracy, to Pima-Indian diabetes with 77.8% accuracy, to diagnosis of breast cancer with 97.8% accuracy and to echocardiogram with 93.0% accuracy. Results can be considered good and they compare well with results reported with literature.     

Feature fusion of side face and gait for video-based human identification

Video-based human recognition at a distance remains a challenging problem for the fusion of multimodal biometrics. As compared to the approach based on match score level fusion, in this paper, we present a new approach that utilizes and integrates information from side face and gait at the feature level. The features of face and gait are obtained separately using principal component analysis (PCA) from enhanced side face image (ESFI) and gait energy image (GEI), respectively. Multiple discriminant analysis (MDA) is employed on the concatenated features of face and gait to obtain discriminating synthetic features. This process allows the generation of better features and reduces the curse of dimensionality. The proposed scheme is tested using two comparative data sets to show the effect of changing clothes and face changing over time. Moreover, the proposed feature level fusion is compared with the match score level fusion and another feature level fusion scheme. The experimental results demonstrate that the synthetic features, encoding both side face and gait information, carry more discriminating power than the individual biometrics features, and the proposed feature level fusion scheme outperforms the match score level and another feature level fusion scheme. The performance of different fusion schemes is also shown as cumulative match characteristic (CMC) curves. They further demonstrate the strength of the proposed fusion scheme.     

Sparse representation-based classification for breath sample identification

It has been discovered that some compounds in human breath can be used to detect some diseases and monitor the development of the conditions. A sensor system in tandem with certain data evaluation algorithm offers an approach to analyze the compositions of breath. Currently, most algorithms rely on the generally designed pattern recognition techniques rather than considering the specific characteristics of data. They may not be suitable for odor signal identification. This paper proposes a Sparse Representation-based Classification (SRC) method for breath sample identification. The sparse representation expresses an input signal as the linear combination of a small number of the training signals, which are from the same category as the input signal. The selection of a proper set of training signals in representation, therefore, gives us useful cues for classification. Two experiments were conducted to evaluate the proposed method. The first one was to distinguish diabetes samples from healthy ones. The second one aimed to classify these diseased samples into different groups, each standing for one blood glucose level. To illustrate the robustness of this method, two different feature sets, namely, geometry features and principle components were employed. Experimental results show that the proposed SRC outperforms other common methods, such as K Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), Artificial Neural Network (ANN), and Support Vector Machine (SVM), irrespective of the features selected.     

Fuzzy classification maximum likelihood algorithms for mixed-Weibull distributions

In this paper we propose an efficient algorithm based on Yang’s (Fuzzy Sets Syst 57:365–337, 1993) concept, namely the fuzzy classification maximum likelihood (FCML) algorithm, to estimate the mixed-Weibull parameters. Compared with EM and Jiang and Murthy (IEEE Trans Reliab 44:477–488, 1995) methods, the proposed FCML algorithm presents better accuracy. Thus, we recommend FCML as another acceptable method for estimating the mixed-Weibull parameters.     

Fuzzy ELM for classification based on feature space

Multimedia Tools and Applications - As a competitive machine learning algorithm, extreme learning machine (ELM), with its simple theory and easy implementation, has been widely used in the field of...