A new facial expression recognition based on curvelet transform and online sequential extreme learning machine initialized with spherical clustering

In this paper, a novel algorithm is proposed for facial expression recognition by integrating curvelet transform and online sequential extreme learning machine (OSELM) with radial basis function (RBF) hidden node having optimal network architecture. In the proposed algorithm, the curvelet transform is firstly applied to each region of the face image divided into local regions instead of whole face image to reduce the curvelet coefficients too huge to classify. Feature set is then generated by calculating the entropy, the standard deviation and the mean of curvelet coefficients of each region. Finally, spherical clustering (SC) method is employed to the feature set to automatically determine the optimal hidden node number and RBF hidden node parameters of OSELM by aim of increasing classification accuracy and reducing the required time to select the hidden node number. So, the learning machine is called as OSELM-SC. It is constructed two groups of experiments: The aim of the first one is to evaluate the classification performance of OSELM-SC on the benchmark datasets, i.e., image segment, satellite image and DNA. The second one is to test the performance of the proposed facial expression recognition algorithm on the Japanese Female Facial Expression database and the Cohn-Kanade database. The obtained experimental results are compared against the state-of-the-art methods. The results demonstrate that the proposed algorithm can produce effective facial expression features and exhibit good recognition accuracy and robustness.     

基于多特征融合与极限学习机的植物叶片分类方法

对植物的分类多通过对植物叶片的分类来实现,为提高植物叶片分类的准确率提出了一种基于多特征融合与极限学习机的植物叶片分类方法.首先对植物叶片彩色图像进行预处理,得到去除叶片颜色与背景的二值图像和灰度图像;然后从二值图像中提取植物叶片的形状特征和不变矩特征,利用灰度图像提取灰度共生矩阵参数作为叶片图像的纹理特征,共得到28...     

基于Contourlet变换和支持向量机的纹理识别方法

针对变换域中图像纹理识别时如何选择最佳特征向量的问题,利用Contourlet变换的多方向、多尺度选择性和各向异性,将图像从空间域变换到频率域,全面地提取了Contourlet变换分解后低频子带、中频子带和高频子带的特征,输入支持向量机(SVM)分类器进行分类识别。利用Brodatz纹理库进行仿真实验,实验结果表明低频均值方差和高频能量作为组合特征时识别准确率可达98.75%,且特征向量维数少,是在Contourlet变换下表示图像纹理的最优特征。     

Deterministic multikernel extreme learning machine with fuzzy feature extraction for pattern classification

In this paper a novel multikernel deterministic extreme learning machine (ELM) and its variants are developed for classification of non-linear problems. Over a decade ELM is proved to be efficacious learning algorithms, but due to the non-deterministic and single kernel dependent feature mapping proprietary, it cannot be efficiently applied to real time classification problems that require invariant output solution. We address this problem by analytically calculation of input and hidden layer parameters for achieving the deterministic solution and exploiting the data fusion proficiency of multiple kernel learning. This investigation originates a novel deterministic ELM with single layer architecture in which kernel function is aggregation of linear combination of disparate base kernels. The weight of kernels depends upon perspicacity of problem and is empirically calculated. To further enhance the performance we utilize the capabilities of fuzzy set to find the pixel-wise coalition of face images with different classes. This handles the uncertainty involved in face recognition under varying environment condition. The pixel-wise membership value extracts the unseen information from images up to significant extent. The validity of the proposed approach is tested extensively on diverse set of face databases: databases with and without illumination variations and discrete types of kernels. The proposed algorithms achieve 100% recognition rate for Yale database, when seven and eight images per identity are considered for training. Also, the superior recognition rate is achieved for AT & T, Georgia Tech and AR databases, when compared with contemporary methods that prove the efficacy of proposed approaches in uncontrolled conditions significantly.

     

基于隐特征空间的极限学习机模型选择

针对极限学习机（ELM）中冗余的隐神经元会削弱模型泛化能力的缺点,提出了一种基于隐特征空间的ELM模型选择算法。首先,为了寻找合适的ELM隐层,在ELM中添加正则项,该项为现有隐层空间到低维隐特征空间的映射函数矩阵的Frobenius范数;其次,为解决该非凸问题,采用交替优化的策略,并通过凸二次型优化学习该隐空间;最终自适应得到最优映射函数和ELM模型。分别采用UCI标准数据集和载荷识别工程数据对所提算法进行测试,结果表明,与经典ELM相比,该算法可有效提高预测精度和数值稳定性,与现有模型选择算法相比,该算法预测精度相当,但运行时间则大幅降低。     

基于第二代Curvelet变换的点云曲面特征提取

针对点云特征提前取方法在多方向性分析方面的局限性,将Curvelet变换引入点云的分析,研究数据点云不同尺度曲面特征的提取方法。在数据点云分层、扩展预处理的基础上,以第二代离散Curvelet变换分析数据点云,采用软硬阈值折衷法,对表示数据点云边缘的Detail层、Fine层Curvelet变换系数进行处理,增强数据点云的边缘。对增强后的Curvelet变换系数进行Curvelet逆变换,重构数据点云,提取数据点云的边缘,获取曲面特征。实例表明,以Curvelet变换分析为基础的曲面特征提取方法,可以更加准确地提取数据点云的曲面特征。     

基于离散多小波变换的纹理分类

纹理分析在遥感、医学图像处理、计算机视觉及基于纹理的按内容检索的图像数据库等许多重要领域均有着广泛的应用.引入多小波理论,提出了基于多小波分解的纹理图像分类.通过一系列的实验并与单小波进行比较,实验结果表明,多小波分解比金字塔小波分解或小波包分解其分类准确率更优.     

Multiple-kernel-learning-based extreme learning machine for classification design

The extreme learning machine (ELM) is a new method for using single hidden layer feed-forward networks with a much simpler training method. While conventional kernel-based classifiers are based on a single kernel, in reality, it is often desirable to base classifiers on combinations of multiple kernels. In this paper, we propose the issue of multiple-kernel learning (MKL) for ELM by formulating it as a semi-infinite linear programming. We further extend this idea by integrating with techniques of MKL. The kernel function in this ELM formulation no longer needs to be fixed, but can be automatically learned as a combination of multiple kernels. Two formulations of multiple-kernel classifiers are proposed. The first one is based on a convex combination of the given base kernels, while the second one uses a convex combination of the so-called equivalent kernels. Empirically, the second formulation is particularly competitive. Experiments on a large number of both toy and real-world data sets (including high-magnification sampling rate image data set) show that the resultant classifier is fast and accurate and can also be easily trained by simply changing linear program.     

Generalized eigenvalue extreme learning machine for classification

Applied Intelligence - Extreme learning machine (ELM) has attracted widespread attention due to its simple, quick and good performance. In this work, in order to deal with cross data quickly and...     

Brain tumor classification using modified kernel based softplus extreme learning machine

Multimedia Tools and Applications - An uncontrollable growth of abnormal cells in the brain may result in brain tumor. Two different categories of brain tumor are benign and malignant. The doctors...     

Probability based voting extreme learning machine for multiclass XML documents classification

This paper presents a novel solution based on Extreme Learning Machine (ELM) for multiclass XML documents classification. ELM is a generalized Single-hidden Layer Feedforward Network (SLFN) with extremely fast learning capacity. An improved vector model DSVM (Distribution based Structured Vector Model) is proposed to represent XML documents with more structural information and more precise semantic information. The XML documents classifiers are conducted based on PV-ELM (Probablity based Voting ELM) with a postprocessing method ε-RCC (ε - Revoting of Confusing Classes) to refine the voting results. To evaluate the overall performance of this solution, a series of experiments are conducted on two real datasets of news feeds online. The experimental results show that DSVM represents the XML documents more effectively and PV-ELM with ε-RCC achieves a higher accuracy than original ELM algorithm for multiclass classification.     

Representation learning with deep extreme learning machines for efficient image set classification

Efficient and accurate representation of a collection of images, that belong to the same class, is a major research challenge for practical image set classification. Existing methods either make prior assumptions about the data structure, or perform heavy computations to learn structure from the data itself. In this paper, we propose an efficient image set representation that does not make any prior assumptions about the structure of the underlying data. We learn the nonlinear structure of image sets with deep extreme learning machines that are very efficient and generalize well even on a limited number of training samples. Extensive experiments on a broad range of public datasets for image set classification show that the proposed algorithm consistently outperforms state-of-the-art image set classification methods both in terms of speed and accuracy.

     

Face recognition based on extreme learning machine

Extreme learning machine (ELM) is an efficient learning algorithm for generalized single hidden layer feedforward networks (SLFNs), which performs well in both regression and classification applications. It has recently been shown that from the optimization point of view ELM and support vector machine (SVM) are equivalent but ELM has less stringent optimization constraints. Due to the mild optimization constraints ELM can be easy of implementation and usually obtains better generalization performance. In this paper we study the performance of the one-against-all (OAA) and one-against-one (OAO) ELM for classification in multi-label face recognition applications. The performance is verified through four benchmarking face image data sets.     

Land-use scene classification based on a CNN using a constrained extreme learning machine

ABSTRACT

Classifying land-use scenes from high-resolution remote-sensing imagery with high quality and accuracy is of paramount interest for science and land management applications. In this article, we proposed a new model for land-use scene classification by integrating the recent success of convolutional neural network (CNN) and constrained extreme learning machine (CELM). In the model, the fully connected layers of a pretrained CNN have been removed. Then, CNN works as a deep and robust convolutional feature extractor. After normalization, deep convolutional features are fed to the CELM classifier. To analyse the performance, the proposed method has been evaluated on two challenging high-resolution data sets: (1) the aerial image data set consisting of 30 different aerial scene categories with sub-metre resolution and (2) a Sydney data set that is a large high spatial resolution satellite image. Experimental results show that the CNN-CELM model improves the generalization ability and reduces the training time compared to state-of-the-art methods.     

Fingerprint matching based on extreme learning machine

Considering fingerprint matching as a classification problem, the extreme learning machine (ELM) is a powerful classifier for assigning inputs to their corresponding classes, which offers better generalization performance, much faster learning speed, and minimal human intervention, and is therefore able to overcome the disadvantages of other gradient-based, standard optimization-based, and least squares-based learning techniques, such as high computational complexity, difficult parameter tuning, and so on. This paper proposes a novel fingerprint recognition system by first applying the ELM and Regularized ELM (R-ELM) to fingerprint matching to overcome the demerits of traditional learning methods. The proposed method includes the following steps: effective preprocessing, extraction of invariant moment features, and PCA for feature selection. Finally, ELM and R-ELM are used for fingerprint matching. Experimental results show that the proposed methods have a higher matching accuracy and are less time-consuming; thus, they are suitable for real-time processing. Other comparative studies involving traditional methods also show that the proposed methods with ELM and R-ELM outperform the traditional ones.     

Surface reconstruction based on extreme learning machine

In this paper, extreme learning machine (ELM) is used to reconstruct a surface with a high speed. It is shown that an improved ELM, called polyharmonic extreme learning machine (P-ELM), is proposed to reconstruct a smoother surface with a high accuracy and robust stability. The proposed P-ELM improves ELM in the sense of adding a polynomial in the single-hidden-layer feedforward networks to approximate the unknown function of the surface. The proposed P-ELM can not only retain the advantages of ELM with an extremely high learning speed and a good generalization performance but also reflect the intrinsic properties of the reconstructed surface. The detailed comparisons of the P-ELM, RBF algorithm, and ELM are carried out in the simulation to show the good performances and the effectiveness of the proposed algorithm.     

基于核极限学习机的标记分布学习

标记分布学习作为一种新的学习范式,利用最大熵模型构造的专用化算法能够很好地解决某些标记多样性问题,但是计算量巨大。基于此,引入运行速度快、稳定性更高的核极限学习机模型,提出基于核极限学习机的标记分布学习算法（KELM-LDL）。首先在极限学习机算法中通过RBF核函数将特征映射到高维空间,然后对原标记空间建立KELM回归模型求得输出权值,最后通过模型计算预测未知样本的标记分布。与现有算法在各领域不同规模数据集的实验表明,实验结果均优于多个对比算法,统计假设检验进一步说明KELM-LDL算法的有效性和稳定性。     

基于机器学习的音频分类

为施行有效的音频分类以高效率处理日渐复杂的音频信息,研究采用包含多种神经网络在内的5种机器学习模型,实现多种决策下的音频分类以寻找最优模型,基于分类准确度对各模型分类效果进行评估,在使用正则化方法保证模型泛化能力的条件下,通过比较和实验,挖掘并验证出了相对最优的模型——卷积神经网络音频分类模型及对应参数,为现有音频分类模型的进一步优化提供了参考方向。     

基于去噪自编码器的极限学习机

针对极限学习机算法（ELM）参数随机赋值降低算法鲁棒性及性能受噪声影响显著的问题，将去噪自编码器（DAE）与ELM算法相结合，提出了基于去噪自编码器的极限学习机算法（DAE-ELM）。首先，通过去噪自编码器产生ELM的输入数据、输入权值与隐含层参数；然后，以ELM求得隐含层输出权值，完成对分类器的训练。该算法一方面继承了DAE的优点，自动提取的特征更具代表性与鲁棒性，对于噪声有较强的抑制作用；另一方面克服了ELM参数赋值的随机性，增强了算法鲁棒性。实验结果表明，在不含噪声影响下DAE-ELM相较于ELM、PCA-ELM、SAA-2算法，其分类错误率在MNIST数据集中至少下降了5.6%，在Fashion MNIST数据集中至少下降了3.0%，在Rectangles数据集中至少下降了2.0%，在Convex数据集中至少下降了12.7%。