Label Consistent K-singular Value Decomposition Research Articles

A Hierarchical Discriminative Sparse Representation Classifier for EEG Signal Detection.

Classification of electroencephalogram (EEG) signal data plays a vital role in epilepsy detection. Recently sparse representation-based classification (SRC) methods have achieved the good performance in EEG signal automatic detection, by which the EEG signals are sparsely represented using a few active coefficients in the dictionary and classified according to the reconstruction criteria. However, most of SRC learn a linear dictionary for encoding, and cannot extract enough information and nonlinear relationship of data for classification. To solve this problem, a hierarchical discriminative sparse representation classification model (called HD-SRC) for EEG signal detection is proposed. Based on the framework of neural network, HD-SRC learns the hierarchical nonlinear transformation and maps the signal data into the nonlinear transformed space. Through incorporating this idea into label consistent K singular value decomposition (LC-KSVD) at the top layer of neural network, HD-SRC seeks discriminative representation together with dictionary, while minimizing errors of classification, reconstruction and discriminative sparse-code for pattern classification. By learning the hierarchical feature mapping and discriminative dictionary simultaneously, more discriminative information of data can be exploited. In the experiment the proposed model is evaluated on the Bonn EEG database, and the results show it obtains satisfactory classification performance in multiple EEG signal detection tasks.

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.

Label-Consistent Transform Learning for Hyperspectral Image Classification

This letter proposes a new image analysis tool called label-consistent transform learning. Transform learning is a recent unsupervised representation learning approach; we add supervision by incorporating a label consistency constraint. The proposed technique is especially suited for hyperspectral image classification problems owing to its ability to learn from fewer samples. We have compared our proposed method with the state-of-the-art techniques such as label-consistent K-singular value decomposition, stacked autoencoder, deep belief network, convolutional neural network, and generative adversarial network. Our method yields considerably better results than all the aforesaid techniques.

Supervised dictionary learning supported classifier with feature fusion scheme to noninvasively detect TRISO-particle defects

This paper presents a novel method to support analyzing TRISO-particle failure rate by exploiting X-ray phase contrast imaging (PCI) modality to nondestructively visualize inter-defects and supervised dictionary learning to automatically distinguish cracked particles. Histogram of oriented gradient (HOG) operator was combined with local binary pattern histogram Fourier (LBP-HF) descriptor by canonical correlation analysis (CCA) method in order to extract crack features more significantly. Label consistent K-singular value decomposition (LC K-SVD) dictionary learning followed to encode features with more discriminability and learn a dictionary capable of excluding noise and intra-class variability to enforce recognition, with comparatively high recognition accuracy.

Adaptive visual target tracking algorithm based on classified-patch kernel particle filter

We propose a high-performance visual target tracking (VTT) algorithm based on classified-patch kernel particle filter (CKPF). Novel features of this VTT algorithm include sparse representations of the target template using the label-consistent K-singular value decomposition (LC-KSVD) algorithm; Gaussian kernel density particle filter to facilitate candidate template generation and likelihood matching score evaluation; and an occlusion detection method using sparse coefficient histogram (ASCH). Experimental results validate superior performance of the proposed tracking algorithm over state-of-the-art visual target tracking algorithms in scenarios that include occlusion, background clutter, illumination change, target rotation, and scale changes.

Toward Domain Transfer for No-Reference Quality Prediction of Asymmetrically Distorted Stereoscopic Images

We have presented a no-reference quality prediction method for asymmetrically distorted stereoscopic images, which aims to transfer the information from source feature domain to its target quality domain using a label consistent K-singular value decomposition classification framework. To this end, we construct a category-deviation database for dictionary learning that assigns a label for each stereoscopic image to indicate if it is noticeable or unnoticeable by human eyes. Then, by incorporating a category consistent term into the objective function, we learn view-specific feature and quality dictionaries to establish a semantic framework between the source feature domain and the target quality domain. The quality pooling is comparatively simple and only needs to estimate the quality score based on the classification probability. The experimental results demonstrate the effectiveness of our blind metric.

SAR Automatic Target Recognition Based on Dictionary Learning and Joint Dynamic Sparse Representation

In this letter, we propose a novel automatic target recognition (ATR) method based on dictionary learning and joint dynamic sparse representation (DL-JDSR) for synthetic aperture radar (SAR) images. First, in the feature extraction step, we extract two kinds of features, i.e., the image domain amplitude feature and the scale-invariant feature transform (SIFT) feature, of which the image domain amplitude feature describes intensity information and the SIFT feature describes gradient information. These two features will be jointly utilized to combine the two kinds of information for SAR ATR. Second, we introduce the dictionary-learning method, the label-consistent K-singular value decomposition, into the training step to learn dictionaries for the two features rather than directly using all training samples as the fixed dictionaries in the traditional sparse representation method. The learned dictionaries have smaller sizes and are more distinctive among different classes, which can speed up our recognition and improve the accuracies. Third, the JDSR algorithm used in the test step employs a more flexible atom selection method, which enables the two features from an image data to share the similar but not exactly the same sparse mode. Experiments on the moving and stationary target acquisition and recognition data set show that the proposed method is an effective way to recognize SAR images.

Projective dictionary pair learning for EEG signal classification in brain computer interface applications

Electroencephalogram (EEG) based brain-computer interface (BCI) is a useful communication tool between human brain and external devices. Accurate and effective EEG classification plays an important role in performance of BCI applications. In this paper, we propose a dictionary pair learning (DPL) method for EEG signal classification. In this method, we can learn a dictionary without costly L0 and L1 calculation and sparse coefficients have been calculated by linear projection instead of nonlinear sparse coding. We analyzed the performance of new method using EEG data from IIIa and IVa databases of BCI competition III. Experimental results showed that proposed method provides higher classification performance compared with other dictionary learning methods such as label consistent K Singular value decomposition (LC-KSVD). Based on our results, accuracy rates are as follows: 81.25%, 100%, 60.2%, 83.04% and 79.37% for subjects “aa”, “al”, “av”, “aw” and “ay”, respectively from IVa database. Also, the average accuracy rate of 85.7% has been achieved for two-class classification of IIIa database.