Label Consistent K-SVD Research Articles

A major depressive disorder diagnosis approach based on EEG signals using dictionary learning and functional connectivity features.

Major depressive disorder (MDD) as a psychiatric illness negatively affects the behavior and daily life of the patients.Therefore, the early MDD diagnosis can help to cure the patients more efficiently and prevent adverse effects, although its unclear manifestations make the early diagnosis challenging. Nowadays, many studies have proposed automatic early MDD diagnosis methods based on electroencephalogram (EEG) signals. This study also presents an automated EEG-based MDD diagnosis framework based on Dictionary learning (DL) approaches and functional connectivity features. Firstly, a feature space of MDD and healthy control (HC) participants were constructed via functional connectivity features.Next, DL-based classification approaches such as Label Consistent K-SVD (LC-KSVD) and Correlation-based Label Consistent K-SVD (CLC-KSVD) methods, were utilized to perform the classification task. A public dataset was used, consisting of EEG signals from 34 MDD patients and 30 HC subjects, to evaluate the proposed method. To validate the proposed method, 10-fold cross-validation technique with 100 iterations was employed, providing accuracy (AC), sensitivity (SE), specificity (SP), F1-score (F1), and false discovery rate (FDR) performance metrics. The results show that LC-KSVD2 and CLC-KSVD2 performed efficiently in classifying MDD and HC cases. The best classification performance was obtained by the LCKSVD2 method, with average AC of 99.0%, SE of 98.9%, SP of 99.2%, F1 of 99.0%, and FDR of 0.8%. According to the results, the proposed method provides an accurate performance and, therefore, it can be developed into a computer-aided diagnosis (CAD) tool for automatic MDD diagnosis.

Active Sonar Target Classification Method Based on Fisher’s Dictionary Learning

The marine environment is complex and changeable, and the interference of noise and reverberation seriously affects the classification performance of active sonar equipment. In particular, when the targets to be measured have similar characteristics, underwater classification becomes more complex. Therefore, a strong, recognizable algorithm needs to be developed that can handle similar feature targets in a reverberation environment. This paper combines Fisher’s discriminant criterion and a dictionary-learning-based sparse representation classification algorithm, and proposes an active sonar target classification method based on Fisher discriminant dictionary learning (FDDL). Based on the learning dictionaries, the proposed method introduces the Fisher restriction criterion to limit the sparse coefficients, thereby obtaining a more discriminating dictionary; finally, it distinguishes the category according to the reconstruction errors of the reconstructed signal and the signal to be measured. The classification performance is compared with the existing methods, such as SVM (Support Vector Machine), SRC (Sparse Representation Based Classification), D-KSVD (Discriminative K-Singular Value Decomposition), and LC-KSVD (label-consistent K-SVD), and the experimental results show that FDDL has a better classification performance than the existing classification methods.

Radioisotope identification using sparse representation with dictionary learning approach for an environmental radiation monitoring system

A radioactive isotope identification algorithm is a prerequisite for a low-resolution scintillation detector applied to an unmanned radiation monitoring system. In this paper, a sparse representation with dictionary learning approach is proposed and applied to plastic gamma-ray spectra. Label-consistent K-SVD was used to learn a discriminative dictionary for the spectra corresponding to a mixture of four isotopes (133Ba, 22Na, 137Cs, and 60Co). A Monte Carlo simulation was employed to produce the simulated data as learning samples. Experimental measurement was conducted to obtain practical spectra. After determining the hyper parameters, two dictionaries tailored to the learning samples were tested by varying with the source position and the measurement time. They achieved average accuracies of 97.6% and 98.0% for all testing spectra. The average accuracy of each dictionary was above 96% for spectra measured over 2 s. They also showed acceptable performance when the spectra were artificially shifted. Thus, the proposed method could be useful for identifying radioisotopes in gamma-ray spectra from a plastic scintillation detector even when a dictionary is adapted to only simulated data. Furthermore, owing to the outstanding properties of sparse representation, the proposed approach can easily be built into an in-situ monitoring system.

Sign language recognition using Kinect sensor based on color stream and skeleton points

This paper presents a sign language recognition system based on color stream and skeleton points. Several approaches have been established to address sign language recognition problems. However, most of the previous approaches still have poor recognition accuracy. The proposed approach uses Kinect sensor based on color stream and skeleton points from the depth stream to improved recognition accuracy. Techniques within this approach use hand trajectories and hand shapes in combating sign recognition challenges. Therefore, for a particular sign a representative feature vector is extracted, which consists of hand trajectories and hand shapes. A sparse dictionary learning algorithm, Label Consistent K-SVD (LC-KSVD) is applied to obtain a discriminative dictionary. Based on that, the system was further developed to a new classification approach for better results. The proposed system was fairly evaluated based on 21 sign words including one-handed signs and two-handed signs. It was observed that the proposed system gets high recognition accuracy of 98.25%, and obtained an average accuracy of 95.34% for signer independent recognition.
 Keywords: Sign language, Color stream, Skeleton points, Kinect sensor, Discriminative dictionary.

Multimode Process Monitoring and Mode Identification Based on Multiple Dictionary Learning

In modern industrial systems, the processes often operate under different modes. Potential fault diagnosis and mode identification are extremely vital to maintain the system safe and reliable. Recently, many methods have been proposed to address these problems separately. Moreover, many of them make an assumption that the data from industrial site only contain the Gaussian noise. However, this assumption is not held in practice, which further reduces their performances. Considering the complicated noise feature of industrial data, we came up with an improved dictionary learning method to settle these problems simultaneously. First, the measurement data were decomposed into three parts: clean data, mode-based noise, and dense Gaussian noise. Then, the dictionary learning method was proposed to characterize each part separately. Inspired by the framework of label-consistent K-SVD, the mode information was incorporated into the dictionary learning method, and we developed a solution to settle the multiple dictionary learning optimization problem. Finally, when new samples arrive, we reconstruct them under the learned dictionary so that each sample's mode and abnormal data and can be determined. The experiments on two different types of simulated process and aluminum electrolysis process show the strength and reliability of our method, which indicates the engineering application value of the proposed method.

Multi-pose facial expression recognition using rectangular HOG feature extractor and label-consistent KSVD classifier

In this paper, a new approach to the classification of facial expressions from multiple pose images is proposed. In this approach, a rectangular histogram of oriented gradient (R-HOG) algorithm is first designed to extract features of face images. The parameters of the R-HOG algorithm, which is a modification of the original HOG algorithm include cell shape, cell size, block size, and the number of orientation bins. The R-HOG is capable of capturing more discriminative texture features of different facial expressions. In addition, a supervised dictionary learning classifier, the label-consistent K-SVD (LC-KSVD) algorithm, is adopted to recognise the facial expression of the subject. To investigate its effectiveness, the proposed technique was applied to classify emotional states of the face images in the two public available facial expression datasets: KDFE and RafD. The experiment study showed that the new method outperformed in many aspects those methods reported in the literature tested with the same datasets. First, the new method handles pose variations better. Second, it is more robust in cases where the size of a training dataset is small. Finally, it's accuracy performance is more consistent measured by standard deviations.

Multi-pose facial expression recognition using rectangular HOG feature extractor and label-consistent KSVD classifier

Multi-pose facial expression recognition using rectangular HOG feature extractor and label-consistent KSVD classifier

Label embedded dictionary learning for image classification

Recently, label consistent k-svd (LC-KSVD) algorithm has been successfully applied in image classification. The objective function of LC-KSVD is consisted of reconstruction error, classification error and discriminative sparse codes error with ℓ0-norm sparse regularization term. The ℓ0-norm, however, leads to NP-hard problem. Despite some methods such as orthogonal matching pursuit can help solve this problem to some extent, it is quite difficult to find the optimum sparse solution. To overcome this limitation, we propose a method named label embedded dictionary learning (LEDL), which embeds the label information into ℓ1 regularized dictionary learning algorithm to improve the performance of image classification tasks. Specifically, (i) compared to LC-KSVD, we utilise the ℓ1-norm to transfer the sparse constraint problem to convex optimization problem; (ii) alternating direction method of multipliers (ADMM) is adopted to solve the sparse constraint problem to improve the optimization speed; (iii) extensive experimental results on six benchmark datasets illustrate that the classification rate of our proposed algorithm exceeds the LC-KSVD algorithm and our proposed algorithm has achieved state-of-the-art performance.

Multifeature-Based Discriminative Label Consistent K-SVD for Hyperspectral Image Classification

Given the chaotic background and complex content of hyperspectral images (HSIs), the complementary information of multiple features is beneficial for HSI classification. Many multifeature-based sparse representation methods have been recently proposed. However, the large-scale dictionary problem and the optimal multifeature combination strategy have remained unsolved. For the aforementioned problem, we propose a multifeature-based discriminative label consistent K-singular value decomposition (MF-LC-KSVD) algorithm for HSI classification. In the training step of the approach, a multifeature dictionary and a combined linear classifier are obtained by jointly utilizing the LC-KSVD algorithm on the extracted features. The multifeature classifier can automatically determine the relative importance of different features for classification. The “discriminative sparse-code error” constraint in the LC-KSVD facilitates the sharing of the same spare pattern of the atoms in a special feature from the same class, thereby increasing the discrimination of the linear classifier. In the testing step, a multifeature sparse matrix is obtained by introducing the above overcomplete multifeature dictionary into multifeature-based adaptive sparse representation. The class label is then predicted by applying the classifier on multifeature sparse codes. In addition, we introduce the shape-adaptive spatial region to incorporate the information of neighbor pixels, hence further improving the classification performance. Compared with other multifeature-based sparse representation methods, our proposed MF-LC-KSVD is proven to have better classification accuracy.

Discriminative dictionary learning for retinal vessel segmentation using fusion of multiple features

In recent years, automated retinal vessel segmentation has become especially essential for the early detection of some ophthalmological and cardiovascular diseases. In this paper, we have presented a new retinal vessel segmentation method via discriminative dictionary learning using fusion of multiple features, which is able to capture both thick and thin vessel structures. In the training stage, we employ six different enhancement algorithms to obtain multiple complementary features that contain rich vascular information. Then, the manually annotated ground-truth vessels are classified into thick or thin vessels as the label information, and the label consistent KSVD based framework is applied to train the dictionary for vessel segmentation. In the testing stage, comprehensive experiments are conducted on three datasets to measure segmentation performance with eight representative evaluation metrics. The average sensitivity reaches 0.7915, 0.7560 and 0.7202 respectively, suggesting that our method can segment tiny vascular structures well.

Supervised dictionary learning of EEG signals for mild cognitive impairment diagnosis

Mild Cognitive Impairment (MCI) is an intermediate stage of memory decline between normal aging and Alzheimer’s disease or other types of dementia. MCI diagnosis is instrumental in preventing Alzheimer’s; however, its manifestations are complicated (i.e. distinguishing the symptoms is not easy) and MCI is often confused with the normal consequences of aging. To have an accurate and reliable Electroencephalogram (EEG)-based screening tool for MCI diagnosis, we are developing a new supervised dictionary-learning-based analysis of EEG signals, namely Correlation-based Label Consistent K-SVD (CLC-KSVD), which would solve the non-repeatability problem of conventional K-SVD. The proposed method is applied on both time and frequency domains, i.e. 1) the extracted patches from EEG signals recorded at resting state with eyes closed, and 2) the extracted spectral features from these EEG signals. The final label for each EEG signal (in different channels and zones) is obtained by voting between the labels of the whole of time and spectral patches. The evaluation results for the EEG signals of 61 subjects illustrate that CLC-KSVD outperforms other methods (the best accuracy of 88.9% was obtained in F7, T3 channels and the left temporal zone). Furthermore, the results are investigated using the volumetric analysis of Magnetic Resonance (MR) images, indicating that the most significant difference between healthy and MCI groups were in the superior temporal and pars triangularis in the left hemisphere. Such location matching between EEG and MR images demonstrates that the results of CLC-KSVD are anatomically interpretable.

Discriminative dictionary learning for local LV wall motion classification in cardiac MRI

The characterization of cardiac function is of high clinical interest for early diagnosis and better patient follow-up in cardiovascular diseases. A large number of cardiac image analysis methods and more precisely in cine-Magnetic Resonance Imaging (MRI) have been proposed to quantify both shape and motion parameters. However, the first major problem to address lies in the cardiac image segmentation that is most often needed to extract the myocardium before any other process such as motion tracking, or registration. Moreover, intelligent systems based on classification and learning techniques have emerged over the last years in medical imaging. In this paper, a new method is proposed to help medical experts in classifying the Left Ventricle (LV) wall motion without the need of image segmentation and through the learning of motion features by using dictionary learning techniques. Specifically the novelty of this approach lies in the extraction of new spatio-temporal descriptors and in the use of discriminative dictionary learning (DL) techniques to classify normal/abnormal LV function in cardiac MRI. Local radial spatio-temporal profiles are first extracted from bidimensional (2D) short axis (SAX) MRI images, for each anatomical segment of the LV cavity. These profiles inherently contain discriminatory information that can help for cardiac motion characterization. An advantage of this approach is that these profiles are constructed from a very limited user interaction that corresponds to a number of five points in only one frame of the sequence, (without the need of LV boundaries segmentation) and by exploiting all the phases of the cardiac cycle. Two specific discriminative DL algorithms have been selected for the LV wall classification based on these profiles: Label Consistent K-SVD (LC-KSVD) and Fisher Discriminative (FD-DL). For the application of the proposed methods, cine-MR SAX images have been collected from a control group of 9 healthy subjects and from 9 patients with cardiac dyssynchrony. Radial strain curves in 2D Speckle Tracking Echocardiography (2D-STE) have been analysed for the patient group and have been used as reference truth. They allowed to label each profile as normal or abnormal. The best performance has been achieved in the Wavelet domain by the LC-KSVD algorithm with an accuracy of 84.07% in the classification of radial spatio-temporal profiles and using a leave-one-out patient cross validation. The approach has been compared with recent methods of the literature and offers a good compromise between performance, user interaction, time computing and complexity. This new method of LV classification, with minimal user interaction and based on discriminative DL has not been previously reported. It could help to improve the performance of pre-screening systems for cardiac assessment, which can affect positively the quality of the early diagnosis for heart failure patients.

Feature Extraction and Classification of Citrus Juice by Using an Enhanced L-KSVD on Data Obtained from Electronic Nose.

Aroma plays a significant role in the quality of citrus fruits and processed products. The detection and analysis of citrus volatiles can be measured by an electronic nose (E-nose); in this paper, an E-nose is employed to classify the juice which is stored for different days. Feature extraction and classification are two important requirements for an E-nose. During the training process, a classifier can optimize its own parameters to achieve a better classification accuracy but cannot decide its input data which is treated by feature extraction methods, so the classification result is not always ideal. Label consistent KSVD (L-KSVD) is a novel technique which can extract the feature and classify the data at the same time, and such an operation can improve the classification accuracy. We propose an enhanced L-KSVD called E-LCKSVD for E-nose in this paper. During E-LCKSVD, we introduce a kernel function to the traditional L-KSVD and present a new initialization technique of its dictionary; finally, the weighted coefficients of different parts of its object function is studied, and enhanced quantum-behaved particle swarm optimization (EQPSO) is employed to optimize these coefficients. During the experimental section, we firstly find the classification accuracy of KSVD, and L-KSVD is improved with the help of the kernel function; this can prove that their ability of dealing nonlinear data is improved. Then, we compare the results of different dictionary initialization techniques and prove our proposed method is better. Finally, we find the optimal value of the weighted coefficients of the object function of E-LCKSVD that can make E-nose reach a better performance.

Multi-task hybrid dictionary learning for vehicle classification in sensor networks

In this article, we propose a novel multi-task hybrid dictionary learning approach for moving vehicle classification tasks using multi-sensor networks to improve the classification accuracy in complex scenes with low time complexity, which considers both correlations and complementary information among multiple heterogeneous sensors simultaneously to learn a hybrid dictionary within observations of each sensor. The efficient hybrid dictionary consists of a synthesis dictionary and an analysis dictionary, where discriminative codes can be generated by the trained analysis dictionary and class-specific discriminative reconstruction can be achieved by the trained synthesis dictionary. Extensive experiments are conducted on real data sets captured by the multiple heterogeneous sensors, and the results demonstrate that the proposed method can use the multi-feature fusion method to improve the vehicle classification accuracy, and it can learn a hybrid dictionary to make sure that the sparse coding matrix is obtained by simple linear mapping function. Moreover, the problem of [Formula: see text]-norm[Formula: see text] sparse coding can been solved, to reduce the time complexity of this algorithm, compared with support vector machine, sparse representation classification, label consistent KSVD, Fisher discrimination dictionary learning, hybrid dictionary learning, multi-task sparse representation classification, and multi-task Fisher discrimination dictionary learning algorithms.

Optimizing Multistage Discriminative Dictionaries for Blind Image Quality Assessment

State-of-the-art algorithms for blind image quality assessment (BIQA) typically have two categories. The first category approaches extract natural scene statistics (NSS) as features based on the statistical regularity of natural images. The second category approaches extract features by feature encoding with respect to a learned codebook. However, several problems need to be addressed in existing codebook-based BIQA methods. First, the high-dimensional codebook-based features are memory-consuming and have the risk of over-fitting. Second, there is a semantic gap between the constructed codebook by unsupervised learning and image quality. To address these problems, we propose a novel codebook-based BIQA method by optimizing multistage discriminative dictionaries (MSDDs). To be specific, MSDDs are learned by performing the label consistent K-SVD (LC-KSVD) algorithm in a stage-by-stage manner. For each stage, a new quality consistency constraint called “quality-discriminative regularization” term is introduced and incorporated into the reconstruction error term to form a unified objective function, which can be effectively solved by LC-KSVD for discriminative dictionary learning. Then, the latter stage takes the reconstruction residual data in the former stage as input based on which LC-KSVD is repeatedly performed until the final stage is reached. Once the MSDDs are learned, multistage feature encoding is performed to extract feature codes. Finally, the feature codes are concatenated across all stages and aggregated over the entire image for quality prediction via regression. The proposed method has been evaluated on five databases and experimental results well confirm its superiority over existing relevant BIQA methods.

Learning an event-oriented and discriminative dictionary based on an adaptive label-consistent K-SVD method for event detection in soccer videos

In this paper, we formulate the soccer video event detection task as a sparse representation problem by learning a supervised, discriminative and event-oriented dictionary based on learned weighted local features. To this end, we present a novel framework based on two ideas: First, we propose an approach for computing the representativeness of each video frame for each soccer event. Second, we propose an Adaptive Label-Consistent K-SVD (ALC-KSVD) algorithm to learn an event-oriented and discriminative dictionary based on the computed representativeness of frames to transfer video frames to a sparse space. To improve discrimination among frames of different events, we proposed a weighting method to identify local features that are more representative in each event category. Next, the representativeness score of each frame is calculated by aggregating the weighted local features within each frame. The calculated representativeness score of each frame indicates its belonging degree to each event. The representativeness score matrix, being a discriminative term, is combined with the reconstruction error to form an objective function to improve the discrimination ability in the sparse representation during the dictionary learning process. The obtained objective function is efficiently and optimally solved by the K-SVD algorithm. The representativeness score matrix, which is automatically calculated based on the training samples, defines an adaptive correspondence between the dictionary atoms and the labels of the frames. We demonstrate the effectiveness of the proposed framework on the detection and classification of several soccer events based on an extensive experimental investigation that was conducted using a large collection of video data. The experimental results indicate that our approach maintains good classification performance and outperforms the state-of-the-art methods.

Euler Label Consistent K-SVD for image classification and action recognition

Abstract Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which may help improve the separability and enlarge the margin between nearby data points, we present an effective kernel dictionary learning approach, namely Euler Label Consistent K-SVD (ELC-KSVD), for sparse coding and image recognition. ELC-KSVD first maps the images into the complex space by Euler representation, which has a negligible effect for outliers and illumination, and then learns a discriminative dictionary in Euler space. Different from the most existing kernel dictionary learning approaches, which maps data into a hidden high-dimensional space, Euler representation not only is explicit but also does not increase the dimensionality of image space in our ELC-KSVD. This makes ELC-KSVD algorithm efficient and easy to be realized in real applications. Furthermore, an iterative method is provided to solve ELC-KSVD. This iteration algorithm is fast and has good convergence. Extensive experimental results illustrate that ELC-KSVD outperforms some representative methods and achieves impressive performance for image classification and action recognition.

Machinery fault diagnosis based on time–frequency images and label consistent K-SVD

Wavelet time–frequency analysis has been widely used for machinery fault diagnosis. Mechanical vibration signals can be converted to time–frequency images using wavelet transform, so machinery fault diagnosis can be transformed to the problem of image classification. Label consistent K-SVD algorithm has been proven to be effective in image classification, which incorporates a label consistent term namely discriminative sparse code error into the objective function. Therefore, in this paper, a novel bearing fault diagnosis method based on wavelet time–frequency image and label consistent K-SVD is proposed. Firstly, continuous wavelet transform is utilized to generate wavelet time–frequency images that can fully reflect bearing fault characteristics. Then texture feature extraction based on gray level co-occurrence matrix is implemented on the wavelet time–frequency images. Finally, label consistent K-SVD is conducted for classification of the time–frequency images, and thus bearing fault diagnosis is realized. The experiment results show that the texture features based on gray level co-occurrence matrix of wavelet time–frequency images can effectively extract the fault characteristics of rolling bearings, and label consistent K-SVD performs better than other classification methods based on dictionary learning under the same parameters.

3D Ear Identification Using Block-Wise Statistics-Based Features and LC-KSVD

Biometrics authentication has been corroborated to be an effective method for recognizing a person's identity with high confidence. In this field, the use of three-dimensional (3D) ear shape is a recent trend. As a biometric identifier, the ear has several inherent merits. However, although a great deal of efforts have been devoted, there is still large room for improvement in developing a highly effective and efficient 3D ear identification approach. In this paper, we attempt to fill this gap to some extent by proposing a novel 3D ear classification scheme that makes use of the label consistent K-SVD (LC-KSVD) framework. As an effective supervised dictionary learning algorithm, LC-KSVD learns a single compact discriminative dictionary for sparse coding and a multi-class linear classifier simultaneously. To use the LC-KSVD framework, one key issue is how to extract feature vectors from 3D ear scans. To this end, we propose a block-wise statistics-based feature extraction scheme. Specifically, we divide a 3D ear region of interest into uniform blocks and extract a histogram of surface types from each block; histograms from all blocks are then concatenated to form the desired feature vector. Feature vectors extracted in this way are highly discriminative and are robust to mere misalignment between samples. Experiments demonstrate that our approach can achieve better recognition accuracy than the other state-of-the-art methods. More importantly, its computational complexity is extremely low, making it quite suitable for the large-scale identification applications. MATLAB source codes are publicly online available at http://sse.tongji.edu.cn/linzhang/LCKSVDEar/LCKSVDEar.htm .

An improved collaborative representation based classification with regularized least square (CRC–RLS) method for robust face recognition

Fast and robust face recognition is a challenging research topic in the field of computer vision. A recently proposed Collaborative Representation based Classification with Regularized Least Square (CRC–RLS) algorithm shows very lower computational cost but with poor robustness. In order to solve this problem, we propose an improved CRC–RLS method. Firstly, the image Gabor features were extracted and used to construct initial dictionary. Secondly, we learn a discriminative dictionary by a label consistent K-SVD (LC-KSVD) method which combines the sparse coding error with the reconstruction error and the classification error. Finally, l2-norm of coding residual in CRC–RLS is computed and the classification problem is transformed into solving linear programing problem. Experiments on two benchmark face databases with variations of illumination, expression, occlusion show that the proposed method can achieve high classification accuracy and has a very low time-consuming.