Supervised and Projected Sparse Coding for Image Classification

Semi-supervised two phase test sample sparse representation classifier

Based on multiple maximum scatter difference discrimination Dictionary learning, a novel face recognition algorithm is proposed. Dictionary used for sparse coding plays a key role in sparse representation classification. In this paper, a multiple maximum scatter difference discriminated criterion is used for dictionary learning. During the process of dictionary learning, the multiple maximum scatter difference computes its discriminated vectors from both the range of the between class scatter matrix and the null space of the within-class scatter matrix. The proposed algorithm is theoretically elegant and easy to calculate. Extensive experimental studies conducted on the AR database and Extended Yale Database B in comparison with existing basic sparse representation and other classification methods, it shows that the performance is a little better than the original sparse representation methods with lower complexity.

Gender classification is challenging. Methods for gender classification need to discriminate subtle differences between male and female. Bag-of-Features (BoF) method with sparse coding has been proven very powerful in image classification. In this paper, we apply BoF method for gender classification. We use two sets of images: training images and testing images. All images are represented by a set of Scale-Invariant Feature Transform (SIFT) descriptors. In training stage, using sparse coding, Visual Words Dictionary (VWD) is constructed from SIFT descriptors extracted from training images. In testing, SIFT descriptors of testing images are approximated by visual words in VWD. The choices of approximating visual words determine the classification decision. We apply our method and another two popular methods on public dataset for gender classification. We achieved promising results.

Recently, some sparse coding methods with geometrical constraint have been proposed, in which local geometrical structure of the data points was preserved during sparse coding process. These methods have been applied to classification problems and gained much success. However, they failed to use label information which has been proved to be useful in supervised sparse coding and discriminant manifold learning. In this paper, we propose a discriminant sparse coding approach with geometrical constraint. Labels are used to learn an intrinsic graph and a penalty graph, and these graphs are then embedded into sparse coding framework as constraints. The local geometric structure within each class is preserved and the separability between different classes is enforced. As a result, the discrimination of sparse coding will be improved. Experiments on benchmark databases demonstrate the effectiveness of the proposed method.

Owing to complicated manual design and feature extraction processes of mechanical multi-fault classification and recognition methods, single-class classifiers do not adapt well to the high dimensionality and high self-similarity among mechanical vibration signals with nonlinear characteristics, resulting in insufficient classification accuracy. Combining the adaptive learning and feature extraction capabilities of the wavelet kernel-based extreme learning machine (WKELM) with the sparse representation classifier (SRC), this paper proposes a hybrid artificial bee colony (ABC)-optimized Morlet wavelet kernel-based extreme learning machine (ABC-MWKELM) with SRC. The two-level classifier is capable of cooperatively classifying multiple mechanical faults. First, a Morlet wavelet kernel-based extreme learning machine (MWKELM) model that matches the kernel function construction condition is presented. An ABC optimization algorithm is adopted to optimize critical parameters of the MWKELM model, thereby improving the accuracy and generalization ability of the model. In addition, the ABC algorithm is used to optimize the MWKELM model as a pre-classifier for classifying test samples. According to the posterior probability of each training sample category output, the threshold decision method selects candidate categories with a high confidence coefficient. When the preset meets the threshold, the classification result of the ABC-optimized MWKELM is directly adopted. Otherwise, the candidate training samples are used to construct a dictionary and the SRC is used to classify the test samples further. The experimental results demonstrate that compared with the ELM, WKELM, (ELM-SRC), and radial basis function (RBF)-KELM and SRC (RBFKELM-SRC), the method presented in this paper has significant advantages in terms of accuracy of mechanical multi-fault classification. The validity and applicability of this method in mechanical multi-fault classification were verified.

Sparse Representation Classifier (SRC) and its variants were considered as powerful classifiers in the domains of computer vision and pattern recognition. However, classifying test samples is computationally expensive due to the $$\ell _1$$ norm minimization problem that should be solved in order to get the sparse code. Therefore, these classifiers could not be the right choice for scenarios requiring fast classification. In order to overcome the expensive computational cost of SRC, a two-phase coding classifier based on classic Regularized Least Square was proposed. This classifier is more efficient than SRC. A significant limitation of this classifier is the fact that the number of the samples that should be handed over to the next coding phase should be specified a priori. This paper overcomes this main limitation and proposes five data-driven schemes allowing an automatic estimation of the optimal size of the local samples. These schemes handle the three cases that are encountered in any learning system: supervised, unsupervised, and semi-supervised. Experiments are conducted on five image datasets. These experiments show that the introduced learning schemes can improve the performance of the two-phase linear coding classifier adopting ad-hoc choices for the number of local samples.

Recognition of emotional content in music is an issue that arises recently. Music received by live applications are often exposed to noise, thus prone to reducing the recognition rate of the application. The solution proposed in this study is a robust music emotion recognition system for live applications. The proposed system consists of two major parts, i.e. subspace-based noise suppression and a hierarchical sparse representation classifier, which is based on sparse coding and a sparse representation classifier (SRC). The music is firstly enhanced by fast subspace based noise suppression. Nine classes of emotion are then used to construct a dictionary, and the vector of coefficients is obtained by sparse coding. The vector can be divided into nine parts, and each of which models a specific emotional class of a signal. Since the proposed descriptor can provide emotional content analysis of different resolutions for emotional music recognition, this work regards vectors of coefficients as feature representations. Finally, a sparse representation based classification method is employed for classification of music into four emotional classes. The experimental results confirm the highly robust performance of the proposed system in emotion recognition in live music.

Graph construction using adaptive Local Hybrid Coding scheme

Codebook learning is one of the central research topics in computer vision and machine learning. In this paper, we propose a new codebook learning algorithm, Randomized Forest Sparse Coding (RFSC), by harvesting the following three concepts: (1) ensemble learning, (2) divide-and-conquer, and (3) sparse coding. Given a set of training data, a randomized tree can be used to perform data partition (divide-and-conquer); after a tree is built, a number of bases are learned from the data within each leaf node for a sparse representation (subspace learning via sparse coding); multiple trees with diversities are trained (ensemble), and the collection of bases of these trees constitute the codebook. These three concepts in our codebook learning algorithm have the same target but with different emphasis: subspace learning via sparse coding makes a compact representation, and reduces the information loss; the divide-and-conquer process efficiently obtains the local data clusters; an ensemble of diverse trees provides additional robustness. We have conducted classification experiments on cancer images as well as a variety of natural image datasets and the experiment results demonstrate the efficiency and effectiveness of the proposed method.KeywordsSparsityRandomnessCodebook LearningCancer Image Classification

In this paper, a new method for facial expression classification is proposed, which uses the histograms of oriented gradients (HOG) algorithm to extract facial expression features and the sparse representation classifier (SRC) to classify facial expressions with a large variation of poses. The HOG algorithm was selected due to its effectiveness in picking up both local and global facial expression features in different orientations and scales, and the SRC was chosen because of its proven effectiveness in face recognition. A novelty of the proposed approach is that given a facial image for classification, its pose is determined first to select a pose-dependent dictionary for the SRC procedure. The paper also discusses ways of selecting parameters for improving the effectiveness of the HOG algorithm. The proposed method was applied to two multi-pose facial expression databases and satisfactory results were obtained for the majority of facial expressions under various poses.

Existing sparse representation methods either fail to incorporate the structure and label information of training samples, or suffer from expensive computation for $$l_1$$- or $$l_{2,1}$$-norm. In this paper, we propose three discriminative sparse representation classification methods with structure and label information based on $$l_2$$-norm regularization for robust face recognition. We propose the first classification method with structure and label information by enforcing competition among the representation results of training samples from different classes in representing a test sample. To make the classification more discriminative, we present the decorrelation classification method with structure and label information by jointly considering the competition and decorrelation regularizations. In addition, by incorporating the locality information of samples, we propose the third method called locality-constrained decorrelation classification method with structure and label information. The proposed methods not only contain the structure and label information of training samples, but also have low computational cost owing to the use of $$l_2$$-norm. All three methods have closed-form solutions, rendering them easy to solve and calculate efficiently. Importantly, the proposed methods can achieve better recognition results than most existing state-of-the-art sparse representation methods. Furthermore, based on the proposed methods, we illustrate the effect of different regularization constraints on the recognition performance. Experiments on the ORL, Extended YaleB, FERET, and LFW databases validate the effectiveness of the proposed methods.

In this work, four mainstream machine learning (ML) techniques are used to evaluate the bulk porosity and static elastic modulus of weathered Yungang Grottoes sandstone. Datasets are gathered from the experiments, which includes 432 groups effective experimental data including 8 inputs features. bulk porosity and static elastic modulus were considered as outputs to determine the weathering degrees of Yungang Grottoes sandstone. The 4 performance criteria were used to evaluate the ML models. Results demonstrate that the Artificial Neural Network (ANN) is the best-fitted models for estimating the bulk porosity and static elastic modulus compared to Multiple Linear Regression (MLR), Support Vector Regression (SVR), Gaussian Process Regression (GPR). The accuracy of the trained model for static elastic modulus is slightly higher than that of bulk porosity. The GPR and ANN model can accurately predict the bulk porosity and static elastic modulus in training stages. The ANN with multi-hidden layers developed is competent with high degree of precision and generalization ability for bulk porosity and static elastic modulus compared to other selected regression-based ML models (MLR, SVR, and GPR). The coefficient of determinations of ANN in the range of (0.9537–0.9641) during the testing stages is more stable and higher than that of (0.8883–0.9453) other built ML models. The prediction efficiency of pretrained ANN model was well adjusted for the actual and forecast datasets at the training and testing stages, and the error range was no more than 0.7% and 0.15 GPa at both stages of prediction for bulk porosity and static elastic modulus respectively. And the ANN based static elastic modulus prediction model’s error proportions significantly decreased and were confined to a modest range between + 10% and − 10%. The proposed surrogate models are valid for the bulk porosity ranging from 7 to 14% and the static elastic modulus ranging from 0.7 to 1.4 Gpa, which can be utilized for the accurate and fast prediction of the weathering degrees of Yungang Grottoes sandstone.

Artificial intelligence approach to predict drag reduction in crude oil pipelines

Electrocardiogram (ECG) is a common and powerful tool for studying heart function and diagnosing several abnormal arrhythmias. In this paper, we present a novel classification model that combines the discriminative convolutional sparse coding (DCSC) framework with the linear support vector machine (LSVM) classification strategy. In the training phase, most existing convolutional sparse coding frameworks are unsupervised in the sense that label information is ignored in the convolutional filter training stage. In this work, we explicitly incorporate a label consistency constraint called “discriminative sparse-code error” into the objective function to learn discriminative dictionary filters for sparse coding. The learned dictionary filters encourage signals from the same class to have similar sparse codes, and signals from different classes to have dissimilar sparse codes. To reduce the computational complexity, we propose to perform a max-pooling operation on the sparse coefficients. Using LSVM as a classifier, we examine the performance of the proposed classification system on the MIT-BIH arrhythmia database in accordance with the AAMI EC57 standard. The experimental results show that the proposed DCSC + LSVM algorithm can obtain 99.32% classification accuracy for cardiac arrhythmia recognition.

A new method classifying power quality disturbances (PQD) based on random matrix transform (RMT) and sparse representation classification (SRC) by L1-minimization is presented. First, the PQD signals are characterized by random matrix lower-dimensional projection based on compressive sensing theory. Then, every test sample from feature vectors is represented as a sparse linear combination of training samples. The PQD type assign to the object class that minimizes the residual between test sample and its sparse representation by solving L1-minimization problem. RMT feature extraction method is extremely efficient to generate and independent of the training dataset. Compared with support vector machine (SVM), the SRC algorithm needs neither training process nor combination of two-class classifiers for multiclass classification. Simulation results show that the proposed feature extraction and classification method has high classification correct ratio in strong noise condition.