Holistic recognition of handwritten character pairs

Researchers have thus far focused on the recognition of alpha and numeric characters in isolation as well as in context. In this paper we introduce a new genre of problems where the input pattern is taken to be a pair of characters. This adds to the complexity of the classification task. The 10 class digit recognition problem is now transformed into a 100 class problem where the classes are {00,…, 99}. Similarly, the alpha character recognition problem is transformed to a 26×26 class problem, where the classes are {AA,…, ZZ}. If lower-case characters are also considered the number of classes increases further. The justification for adding to the complexity of the classification task is described in this paper. There are many applications where the pairs of characters occur naturally as an indivisible unit. Therefore, an approach which recognizes pairs of characters, whether or not they are separable, can lead to superior results. In fact, the holistic method described in this paper outperforms the traditional approaches that are based on segmentation. The correct recognition rate on a set of US state abbreviations and digit pairs, touching in various ways, is above 86%.     

Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition

Feature extraction is an important component of a pattern recognition system. It performs two tasks: transforming input parameter vector into a feature vector and/or reducing its dimensionality. A well-defined feature extraction algorithm makes the classification process more effective and efficient. Two popular methods for feature extraction are linear discriminant analysis (LDA) and principal component analysis (PCA). In this paper, the minimum classification error (MCE) training algorithm (which was originally proposed for optimizing classifiers) is investigated for feature extraction. A generalized MCE (GMCE) training algorithm is proposed to mend the shortcomings of the MCE training algorithm. LDA, PCA, and MCE and GMCE algorithms extract features through linear transformation. Support vector machine (SVM) is a recently developed pattern classification algorithm, which uses non-linear kernel functions to achieve non-linear decision boundaries in the parametric space. In this paper, SVM is also investigated and compared to linear feature extraction algorithms.     

An approach for feature semantics recognition in geometric models

This paper describes a method for the recognition of the semantics of parts (features) of a component from a pure geometric representation. It is suitable for verifying product life-cycle requirements from the early stages of the design process. The proposed method is appropriate to analyse B-rep geometric models, and it is not limited to models described by planar and cylindrical surfaces, but it can handle several types of face shapes. In this work the concept of semanteme is introduced. A semanteme represents the minimal element of engineering meaning that can be recognised in a geometric model. The semantemes recognised in a part of the model, which are potentially of engineering significance, are used to associate an engineering meaning to the part. This approach gives a wide flexibility to the proposed system, which is suitable to be used in different contexts of application, since it is possible to describe the reference context using the semanteme that the system can manage.In the paper the implemented prototype system is briefly described. The prototype system takes advantage of neutral interfaces that allow geometrical and topological information to be retrieved from a commercial CAD system.     

Fractional-order embedding canonical correlation analysis and its applications to multi-view dimensionality reduction and recognition

Due to the noise disturbance and limited number of training samples, within-set and between-set sample covariance matrices in canonical correlation analysis (CCA) usually deviate from the true ones. In this paper, we re-estimate within-set and between-set covariance matrices to reduce the negative effect of this deviation. Specifically, we use the idea of fractional order to respectively correct the eigenvalues and singular values in the corresponding sample covariance matrices, and then construct fractional-order within-set and between-set scatter matrices which can obviously alleviate the problem of the deviation. On this basis, a new approach is proposed to reduce the dimensionality of multi-view data for classification tasks, called fractional-order embedding canonical correlation analysis (FECCA). The proposed method is evaluated on various handwritten numeral, face and object recognition problems. Extensive experimental results on the CENPARMI, UCI, AT&T, AR, and COIL-20 databases show that FECCA is very effective and obviously outperforms the existing joint dimensionality reduction or feature extraction methods in terms of classification accuracy. Moreover, its improvements for recognition rates are statistically significant on most cases below the significance level 0.05.     

Face recognition by generalized two-dimensional FLD method and multi-class support vector machines

This paper presents a novel scheme for feature extraction, namely, the generalized two-dimensional Fisher's linear discriminant (G-2DFLD) method and its use for face recognition using multi-class support vector machines as classifier. The G-2DFLD method is an extension of the 2DFLD method for feature extraction. Like 2DFLD method, G-2DFLD method is also based on the original 2D image matrix. However, unlike 2DFLD method, which maximizes class separability either from row or column direction, the G-2DFLD method maximizes class separability from both the row and column directions simultaneously. To realize this, two alternative Fisher's criteria have been defined corresponding to row and column-wise projection directions. Unlike 2DFLD method, the principal components extracted from an image matrix in G-2DFLD method are scalars; yielding much smaller image feature matrix. The proposed G-2DFLD method was evaluated on two popular face recognition databases, the AT&T (formerly ORL) and the UMIST face databases. The experimental results using different experimental strategies show that the new G-2DFLD scheme outperforms the PCA, 2DPCA, FLD and 2DFLD schemes, not only in terms of computation times, but also for the task of face recognition using multi-class support vector machines (SVM) as classifier. The proposed method also outperforms some of the neural networks and other SVM-based methods for face recognition reported in the literature.     

基于混沌粒子群优化的SVM分类器研究

支持向量机(SVM)分类器能较好地解决小样本、非线性、高维等分类问题,具有很强的实用性。然而,支持向量机训练参数的选择对其分类精度有着很大的影响。常用的支持向量机优化方法有遗传算法、粒子群算法都存在易陷入局部极值,优化效果较差的不足。为解决上述问题在粒子群优化算法中引入混沌思想,提出了基于混沌粒子群优化算法(CPSO)的SVM分类器优化方法,CPSO算法能提高种群的多样性和粒子搜索的遍历性,从而有效地提高了PSO算法的收敛速度和精度,更好的优化SVM分类器。并以网络异常入侵检测为研究对象进行仿真,实验结果表明,根据混沌粒子群优化的SVM分类器比传统算法优化的SVM分类器的精度高,速度快。     

Online motion recognition using an accelerometer in a mobile device

This paper introduces a new method to implement a motion recognition process using a mobile phone fitted with an accelerometer. The data collected from the accelerometer are interpreted by means of a statistical study and machine learning algorithms in order to obtain a classification function. Then, that function is implemented in a mobile phone and online experiments are carried out. Experimental results show that this approach can be used to effectively recognize different human activities with a high-level accuracy.     

混沌耗散离散粒子群算法及其在故障诊断中的应用

针对大型化工过程生产系统的高维度数据及其噪声严重影响故障诊断的性能,采用基于故障特征选择和支持向量机（SVM）的故障诊断方法.为了确保在线故障诊断的实时性和准确性,提出一种新型的混沌耗散离散粒子群（CDDPSO）算法,用于故障诊断中特征变量的搜索.仿真结果表明,CDDPSO算法能有效地搜索到全局最优解,而基于故障特征选择的故障诊断方法具有良好的故障诊断性能.     

Iconic and multi-stroke gesture recognition

Many handwritten gestures, characters, and symbols comprise multiple pendown strokes separated by penup strokes. In this paper, a large number of features known from the literature are explored for the recognition of such multi-stroke gestures. Features are computed from a global gesture shape. From its constituent strokes, the mean and standard deviation of each feature are computed. We show that using these new stroke-based features, significant improvements in classification accuracy can be obtained between 10% and 50% compared to global feature representations. These results are consistent over four different databases, containing iconic pen gestures, handwritten symbols, and upper-case characters. Compared to two other multi-stroke recognition techniques, improvements between 25% and 39% are achieved, averaged over all four databases.     

Empirical tests for feature selection based on a psychological theory of character recognition

It is argued that machine algorithms based on feature detection promise the greatest chance for success in the recognition of isolated, unconstrained handprinted characters. In order to match human performance, the features used cannot be chosen in an arbitrary manner; they must have some psychological significance. A theory of characters based on functional attributes is reviewed, and three psychophysical tests are described for determining the psychological validity of any postulated attribute. The first test indicates if a particular attribute is involved in a particular letter, and the second and third tests investigate the commonality of an attribute among different letters.     

Efficient feature size reduction via predictive forward selection

Most of the widely used pattern classification algorithms, such as Support Vector Machines (SVM), are sensitive to the presence of irrelevant or redundant features in the training data. Automatic feature selection algorithms aim at selecting a subset of features present in a given dataset so that the achieved accuracy of the following classifier can be maximized. Feature selection algorithms are generally categorized into two broad categories: algorithms that do not take the following classifier into account (the filter approaches), and algorithms that evaluate the following classifier for each considered feature subset (the wrapper approaches). Filter approaches are typically faster, but wrapper approaches deliver a higher performance. In this paper, we present the algorithm – Predictive Forward Selection – based on the widely used wrapper approach forward selection. Using ideas from meta-learning, the number of required evaluations of the target classifier is reduced by using experience knowledge gained during past feature selection runs on other datasets. We have evaluated our approach on 59 real-world datasets with a focus on SVM as the target classifier. We present comparisons with state-of-the-art wrapper and filter approaches as well as one embedded method for SVM according to accuracy and run-time. The results show that the presented method reaches the accuracy of traditional wrapper approaches requiring significantly less evaluations of the target algorithm. Moreover, our method achieves statistically significant better results than the filter approaches as well as the embedded method.     

Feature definition in pattern recognition with small sample size

The problem of feature definition in the design of a pattern recognition system where the number of available training samples is small but the number of potential features is excessively large has not received adequate attention. Most of the existing feature extraction and feature selection procedures are not feasible due to computational considerations when the number of features exceeds, say, 100, and are not even applicable when the number of features exceeds the number of patterns. The feature definition procedure which we have proposed involves partitioning a large set of highly correlated features into subsets, or clusters, through hierarchical clustering. Almost any feature selection or extraction procedure, including the constrained maximum variance approach introduced here, can then be applied to each subset to obtain a single representative feature. The original set of correlated features is thus reduced to a small set of nearly uncorrelated features. The utility of this procedure has been demonstrated on a speaker-identification data base which consists of 20 subjects, 156 features, and 180 samples.     

Hierarchical vibrations for part-based recognition of complex objects

We propose a technique for the recognition and segmentation of complex shapes in 2D images using a hierarchy of finite element vibration modes in an evolutionary shape search. The different levels of the shape hierarchy can influence each other, which can be exploited in top-down part-based image analysis. Our method overcomes drawbacks of existing structural approaches, which cannot uniformly encode shape variation and co-variation, or rely on training. We present results demonstrating that by utilizing a quality-of-fit function the model explicitly recognizes missing parts of a complex shape, thus allowing for categorization between shape classes.     

Application of semantic features in face recognition

We propose a new face recognition strategy, which integrates the extraction of semantic features from faces with tensor subspace analysis. The semantic features consist of the eyes and mouth, plus the region outlined by the centers of the three components. A new objective function is generated to fuse the semantic and tensor models for finding similarity between a face and its counterpart in the database. Furthermore, singular value decomposition is used to solve the eigenvector problem in the tensor subspace analysis and to project the geometrical properties to the face manifold. Experimental results demonstrate that the proposed semantic feature-based face recognition algorithm has favorable performance with more accurate convergence and less computational efforts.     

模式识别方法在入侵检测中的应用

将模式识别方法应用到入侵检测领域,用以区分正常和异常的用户或主机行为。采用KDD99作为实验数据集,通过计算信息增益,从原始数据中选取对分类结果影响较大的特征属性;再分别选取两种带监督的模式识别方法支持向量机(SVM)和多层神经网络(MNN)以及两种不带监督的聚类方法Single-Linkage和K-Means进行实验。实验结果表明,上述方法在入侵检测领域中具有很好的应用前景。     

Exploring album structure for face recognition in online social networks

In this paper, we propose an album-oriented face-recognition model that exploits the album structure for face recognition in online social networks. Albums, usually associated with pictures of a small group of people at a certain event or occasion, provide vital information that can be used to effectively reduce the possible list of candidate labels. We show how this intuition can be formalized into a model that expresses a prior on how albums tend to have many pictures of a small number of people. We also show how it can be extended to include other information available in a social network. Using two real-world datasets independently drawn from Facebook, we show that this model is broadly applicable and can significantly improve recognition rates.     

Thinning and stroke segmentation for handwritten Chinese character recognition

Firstly, a thinning technique by means of stroke tracking is proposed. The method is considered to preserve the straightness of strokes and the length, which is useful for the stroke segmentation procedure on the recognition of handwritten Chinese characters.Secondly, a method for stroke segmentation, i.c. a way of breaking down a character to a set of consecutive partial strokes, is proposed, which works well owing to the favourable properties of the proposed thinning technique. The method consists of five procedures: extraction of feature pixels, calculation of stroke directions, piecewise linear representation of strokes, unification of intersections and extraction of the consecutive partial strokes.