Discriminative Dictionary Learning Method Research Articles

Discriminative semisupervised dictionary learning method with graph embedding and pairwise constraints for crop disease image recognition

Crop diseases are serious biological disasters that affect agricultural production and ecological environment safety. Accurate detection and identification of disease types can effectively reduce the harm caused by diseases. However, there are various types of crop diseases and labeling them consumes a lot of labor and material resources. A discriminative semisupervised dictionary learning method with graph embedding and pairwise constraints (DSDL-GEPC) is proposed in this study. DSDL-GEPC adopts semisupervised dictionary learning as the baseline model, which combines the reconstruction error of all training samples and the classification error of labeled training samples. DSDL-GEPC consists of three learning terms. The graph embedding term is constructed based on dictionary atoms to obtain global geometric structural information of data samples. Using the pairwise constraint information of similar/heterogeneous images, the pairwise constraints term is constructed to enhance the dictionary's discriminability in terms of within-class similarity and between-class separability. In addition, the relaxed low-rank classification term is constructed, which introduces the ε-draggings technique and low-rank constraint on the relaxed label matrix to learn a discriminative classifier. Experiments are performed to compare the classification performance of DSDL-GEPC on the PlantVillage dataset. The results validate the effectiveness of DSDL-GEPC for crop disease image recognition.

A novel discriminative dictionary learning method for image classification

A novel discriminative dictionary learning method for image classification

Geometry-Aware Discriminative Dictionary Learning for PolSAR Image Classification

In this paper, we propose a new discriminative dictionary learning method based on Riemann geometric perception for polarimetric synthetic aperture radar (PolSAR) image classification. We made an optimization model for geometry-aware discrimination dictionary learning in which the dictionary learning (GADDL) is generalized from Euclidian space to Riemannian manifolds, and dictionary atoms are composed of manifold data. An efficient optimization algorithm based on an alternating direction multiplier method was developed to solve the model. Experiments were implemented on three public datasets: Flevoland-1989, San Francisco and Flevoland-1991. The experimental results show that the proposed method learned a discriminative dictionary with accuracies better those of comparative methods. The convergence of the model and the robustness of the initial dictionary were also verified through experiments.

Structured discriminative tensor dictionary learning for unsupervised domain adaptation

Unsupervised domain adaptation aims at learning a classification model robust to data distribution shift between a labeled source domain and an unlabeled target domain. Most existing approaches have overlooked the multi-dimensional nature of visual data, building classification models in vector space. Meanwhile, the issue of limited training samples is rarely considered by previous methods, yet it is ubiquitous in practical visual applications. In this paper, we develop a structured discriminative tensor dictionary learning method (SDTDL), which enables domain matching in tensor space. SDTDL produces disentangled and transferable representations by explicitly separating domain-specific factor and class-specific factor in data. Classification is achieved based on sample reconstruction fidelity and distribution alignment, which is seamlessly integrated into tensor dictionary learning. We evaluate SDTDL on cross-domain object and digit recognition tasks, paying special attention to the scenarios of limited training samples and test beyond training sample set. Experimental results show that our method outperforms existing mainstream shallow approaches and representative deep learning methods by a significant margin.

Entropy based dictionary learning for image classification

In this paper, a new discriminative dictionary learning algorithm is introduced. An entropy based criterion is embedded into the objective function to enforce a proper structure for the dictionary items when decomposing signals of different classes. The proposed criterion influences the dictionary items to participate in the decomposition of a smaller number of classes as possible. Unlike the other methods, columns of the dictionary are not restricted to have pre-assigned labels and they are free to be representative of any class or to share features of several classes. The number of shared and discriminative items along with the number of dictionary items for each specific class is learned dynamically during the optimization process, depending on the complexity of the classification task and the distribution of different classes. The experimental results demonstrate that the proposed entropy based dictionary learning (EDL) algorithm outperforms other discriminative dictionary learning methods using several real-world image datasets.

Optimal discriminative feature and dictionary learning for image set classification

Image set classification has drawn increasing attention and it has been widely applied to many real-life domains. Due to the existence of multiple images in a set, which contain various view appearance changes, image set classification is a rather challenging task. One potential solution is to learn powerful representations from multiple images to decrease the intra-class diversity and enlarge the inter-class separation. In this paper, we propose an optimal discriminative feature and dictionary learning (ODFDL) method, which attempts to learn a feature mapping matrix and a dictionary such that in the mapped feature space the inter-class sparse reconstruction error of data is maximized and the intra-class sparse reconstruction error is minimized. This learning strategy enforces the learned sparse representations from image sets have large inter-class separation and small intra-class scatter. Furthermore, to better exploit the non-linear information of data from different image sets, we also present two non-linear ODFDL methods, termed Kernel-ODFDL and Hierarchy-ODFDL to further improve the classification performance. Experiments on five commonly used image sets exhibit that our approaches are comparable with many state-of-the-arts.

A Projective and Discriminative Dictionary Learning for High-Dimensional Process Monitoring With Industrial Applications

Data-driven process monitoring methods have attracted many attentions and gained wide applications. However, the real industrial process data are much more complex which is characterized by multimode, high dimensional, corrupted, and less labeled data. In order to eliminate these unfavourable factors simultaneously, a semisupervised robust projective and discriminative dictionary learning method is proposed. First, a semisupervised strategy is introduced to label unsupervised training data. Then, by utilizing low-rank and sparse features of raw data and outliers, a robust decomposition method is used to obtain clean data. After that, a simultaneously projective and discriminative model is proposed to extracting the feature of the low-rank clean data. Finally, the projection matrix and global dictionary, as well as the threshold are obtained through iterative dictionary learning. This hybrid framework provides a robust model for process monitoring and mode identification, and its efficiency is demonstrated by both synthetic examples and real industrial process cases.

Infrared image super-resolution via discriminative dictionary and deep residual network

Image super-resolution successfully overcomes the limitation of infrared imaging system and satisfies the increasing demand for high-resolution infrared images. In this paper, we propose a novel infrared image super-resolution method via discriminative dictionary and deep residual network, which is benefited from the advantages of both compressive sensing and deep learning for more accurate reconstruction results. A discriminative dictionary learning method is introduced to explicitly learn a shared sub-dictionary with a set of cluster-specific sub-dictionaries from the training dataset to better characterize the specific representation of each image. Besides, multiple constraints are integrated to preserve the intrinsic relationship and structure information among images in the learned dictionary. Moreover, to further compensate for the limitation of dictionary representation capacity, a deep residual network is constructed to capture more high-frequency details of infrared images. Both quantitative and qualitative evaluations demonstrate that the proposed method could achieve favorable performances against other state-of-the-art methods.

Brain status modeling with non-negative projective dictionary learning

Accurate prediction of individuals’ brain age is critical to establish a baseline for normal brain development. This study proposes to model brain development with a novel non-negative projective dictionary learning (NPDL) approach, which learns a discriminative representation of multi-modal neuroimaging data for predicting brain age. Our approach encodes the variability of subjects in different age groups using separate dictionaries, projecting features into a low-dimensional manifold such that information is preserved only for the corresponding age group. The proposed framework improves upon previous discriminative dictionary learning methods by incorporating orthogonality and non-negativity constraints, which remove representation redundancy and perform implicit feature selection. We study brain development on multi-modal brain imaging data from the PING dataset (N = 841, age = 3−21 years). The proposed analysis uses our NDPL framework to predict the age of subjects based on cortical measures from T1-weighted MRI and connectome from diffusion weighted imaging (DWI). We also investigate the association between age prediction and cognition, and study the influence of gender on prediction accuracy. Experimental results demonstrate the usefulness of NDPL for modeling brain development.

Low-rank graph preserving discriminative dictionary learning for image recognition

Discriminative dictionary learning plays a key role in sparse representation-based classification. In this paper, we propose a low-rank graph preserving discriminative dictionary learning (LRGPDDL) method for sparse representation-based image recognition. Specifically, we learn a common sub-dictionary as well as several class-specific sub-dictionaries to explicitly capture the common information shared by all the classes and the class-specific information belonging to the corresponding class. We also impose a low-rank constraint on each sub-dictionary to weaken the negative influence from noise contained in training samples. A discriminative graph preserving criterion and a discriminative reconstruction error term are used for exploiting discriminative information, which can improve the discriminative ability of the learned dictionary effectively. In addition, an incoherence term is also introduced into the proposed dictionary learning model to encourage the learned sub-dictionaries to be as independent as possible. Experimental results on several image datasets verify the effectiveness and robustness of LRGPDDL.

SAR Automatic Target Recognition Based on Attribute Scattering Center Model and Discriminative Dictionary Learning

Synthetic aperture radar (SAR) automatic target recognition plays a crucial role in both national defense and civil applications. Although many methods have been proposed, it remains one of the most challenging tasks to identify different terrain vehicle targets. In this paper, a novel method is proposed based on the attribute scattering center model (ASCM) and discriminative dictionary learning. This method contains three main stages. In the first stage, the low-level local features are generated by the convolution operation between SAR patches and the ASCMs with different parameters. These parameters are selected by genetic algorithm in advance with labeled patches. In the feature coding stage, a discriminative dictionary learning method is proposed with the label consistent and locality constraint on the sparse coefficient to incorporate the label information and local geometric information into the sparse representation, which is called label consistent and locality constraint discriminative dictionary learning (LcLcDDL) method. The low-level local features are sparsely coded using the dictionary learned by LcLcDDL method. In the feature pooling stage, the spatial pyramid matching method with max pooling is utilized to aggregate the coding coefficients of the low-level local features to generate the high-level global feature for each patch. The generated high-level global feature is feed into the linear support vector machine to recognize which classes the patch belongs to. Experimental results conducted on the moving and stationary target acquisition and recognition data set show that the performance of the proposed method is better than several available approaches.

Dictionary learning enhancement framework: Learning a non-linear mapping model to enhance discriminative dictionary learning methods

In this paper, a new framework is presented to enhance the reconstruction and discrimination capabilities of existing discriminative dictionary learning methods. In the proposed framework, a non-linear mapping model is introduced to learn a feature space in a way that any standard discriminative dictionary learning algorithms could achieve higher classification accuracies. The proposed feature mapping process targets to boost the standard dictionary learning methods by facilitating their optimization process. The mapping model uses a modified autoencoder network to provide a higher level of reconstruction and discrimination capabilities for the discriminative dictionary learning methods. The proposed dictionary learning enhancement (DLE) framework could be applied to any discriminative dictionary learning methods with the embedded discriminative term in their objective functions. Our experiments on several real-world image datasets demonstrate that the proposed framework could improve the classification accuracies of standard discriminative dictionary learning methods.

Discriminative Fisher Embedding Dictionary Learning Algorithm for Object Recognition.

Both interclass variances and intraclass similarities are crucial for improving the classification performance of discriminative dictionary learning (DDL) algorithms. However, existing DDL methods often ignore the combination between the interclass and intraclass properties of dictionary atoms and coding coefficients. To address this problem, in this paper, we propose a discriminative Fisher embedding dictionary learning (DFEDL) algorithm that simultaneously establishes Fisher embedding models on learned atoms and coefficients. Specifically, we first construct a discriminative Fisher atom embedding model by exploring the Fisher criterion of the atoms, which encourages the atoms of the same class to reconstruct the corresponding training samples as much as possible. At the same time, a discriminative Fisher coefficient embedding model is formulated by imposing the Fisher criterion on the profiles (row vectors of the coding coefficient matrix) and coding coefficients, which forces the coding coefficient matrix to become a block-diagonal matrix. Since the profiles can indicate which training samples are represented by the corresponding atoms, the proposed two discriminative Fisher embedding models can alternatively and interactively promote the discriminative capabilities of the learned dictionary and coding coefficients. The extensive experimental results demonstrate that the proposed DFEDL algorithm achieves superior performance in comparison with some state-of-the-art dictionary learning algorithms on both hand-crafted and deep learning-based features.

Multi-layer discriminative dictionary learning with locality constraint for image classification

Discriminative dictionary learning (DDL) has demonstrated significantly improved performance for image classification. However, most of the existing DDL methods just adopt the single-layer dictionary learning architecture, which narrows the discriminative ability of the coding vectors. Another limitation of these methods is that the atoms of the learned dictionary are easily affected by the noise in the original data. To this end, a powerful architecture, called the multi-layer discriminative dictionary learning (MDDL) with locality constraint, is proposed for image classification. Through the multi-layer dictionary learning, the robust dictionary is obtained in the final layer, where the separability of coding vectors from different classes is also increased. Meanwhile, benefiting from joint classifier training and multi-layer dictionary learning, the discriminability of the learned coding vectors is further enhanced. Besides, by utilizing the graph Laplacian matrices based on the learned dictionaries, not only the locality information of the original data is preserved, but also it can avoid very large values in the coding vectors to reduce the test error caused by overfitting. In addition, an iterative algorithm is devised to efficiently solve the proposed MDDL. The experimental results demonstrate that our methods can achieve promising classification results on well-known benchmark image datasets.

Fast DDL Classification for SAR Images With ${l}_{1,\infty}$ Constraint

Synthetic aperture radar (SAR) image classification aims at labeling pixels with different categories and this is both, a fundamental step for automatic target recognition (ATR) and a prerequisite for further interpretation. In the past decades, various methods have been proposed for the classification of SAR targets and among them are discriminative dictionary learning (DDL) methods. These DDL methods have recently gained attention from researchers' community due to the fact that they are very powerful on both, representation and discrimination during the classification process of SAR images. However, most of the existing DDL methods adopt l 0 -norm or l 1 -norm to ensure the sparsity, but in general, these DDL methods suffer from a high computational burden. Furthermore, it is important to minimize the execution time in the phase of online testing for the scenario of onboard real-time or near real-time SAR automatic target recognition such as modern unmanned aerial vehicle SAR platforms. That said, on reducing execution time, we are confronted with the problem of enhancing recognition efficiency while maintaining its accuracy. In order to solve this problem, our paper proposes a fast DDL method (named as FaDDL) based on a nonlinear analysis co-sparse model by adopting an l 1,∞ -norm ball as a constraint to replace l 0 -norm or l 1 -norm on the coding coefficient matrix. The experimental results show that our proposed method significantly reduces execution time, without losing the classification accuracy.

Subcortical Brain Segmentation Based on a Novel Discriminative Dictionary Learning Method and Sparse Coding

Recently, many multi-atlas patch-based segmentation methods have been proposed and successfully implemented in various medical image applications. However, a precise segmentation of brain subcortical structures in a magnetic resonance image is still difficult since (1) brain MRI typically suffers low tissue contrast; and (2) image patterns around the boundary of a structure are similar such that similarity-based and reconstruction-based label fusion methods achieve inaccurate results. To overcome the above issues, we propose a novel discriminative dictionary learning method, which can simultaneously learn class-specific dictionaries and a shared dictionary from a set of brain atlases. In particular, we enforce a low-rank constraint on each class-specific dictionary, i.e. claim that its spanning subspace should have low-rank property. For the shared dictionary, a regularization term is used to minimize the between-class scatter of corresponding shared coefficients so that they can learn shared image patterns. The optimization algorithms are developed to solve the problems in the learning step. Under the multi-atlas patch-based segmentation framework, the whole learned dictionary then can be used for labeling the target image. The proposed low-rank discriminative dictionary and shared dictionary learning method has been evaluated on IBSR, LPBA40, and SATA MICCAI 2013 dataset for subcortical segmentation. The influence of different parameters was studied and the performance of the proposed method was also compared with the non-local patch-based segmentation, the sparse representation classifier based segmentation, the discriminative dictionary learning segmentation, and several deep learning methods. Experimental results establish the advantages of our method over these state-of-the-art methods.

Class-Oriented Discriminative Dictionary Learning for Image Classification

Dictionary learning has emerged as a powerful tool for a range of image processing applications and a proper dictionary always plays a key issue to the final achievable performance. In this paper, a class-oriented discriminative dictionary learning (CODDL) method is presented for image classification applications. It takes a comprehensive consideration of multiple optimization objectives, emphasizing class discrimination of both dictionary atoms and representation coefficients. The atoms of the learned dictionary should be grouped into class level sub-dictionaries. Meanwhile, the sparse representation coefficients of an input sample should be concentrated on the sub-dictionary of the class it belongs to. Then, based on the learned class-oriented discriminative dictionary, the structured representation coefficients can thus be used for image classification with a simple and efficient classification scheme. The superior performance of the proposed algorithm is demonstrated through extensive experiments.

Detection of Concealed Information Using Multichannel Discriminative Dictionary and Spatial Filter Learning

P300-based concealed information test is a well-known approach for detection of deception. Previous studies only used the parietal channel, Pz, or at most three midline frontal, central, and parietal electrodes (Fz, Cz, and Pz); however, various observations indicate that the P300 component is distributed over a wide area on the scalp. In this paper, we propose a discriminative sparse representation model that effectively makes use of the multichannel concealed information test data. More specifically, a discriminative spatial filter is incorporated into the sparse model. Based on the proposed objective function, the elements of the model (dictionary, spatial filter and sparse code) are iteratively updated such that the discrimination power of the multichannel sparse model is improved. Furthermore, the effect of sparsity constraint on the spatial filter coefficients was investigated. In order to evaluate the performance of the multichannel model in distinguishing guilty subjects from innocent ones, 44 subjects went through a mock crime scenario, and subsequently their electroencephalography signals were recorded using eight electrodes. Experimental results show that the multichannel discriminative dictionary and spatial filter learning method was superior to other multichannel methods allowing classification of guilty and innocent participants with 95% accuracy. Moreover, the learned spatial filters are consistent with the physiological evidence regarding the distribution of P300 subcomponents over the scalp.

Exploiting class-wise coding coefficients: Learning a discriminative dictionary for pattern classification

Abstract Over the past decade, discriminative dictionary learning (DDL) has demonstrated the great success in various pattern classification problems. However, in previous DDL methods, the scheme that how to generate the effective coding coefficients for classification has not been well addressed. This paper proposes a novel DDL method, named CW-DDL, to learn a discriminative dictionary for classification by exploiting class-wise coding coefficients. In the proposed method, a label-aware constraint is first presented to make the coefficient matrix has class-wise approximate sparse structure. Then the graph regularization is further enforced on coding coefficients by utilizing the locality information of dictionary atoms. These two constrained terms reinforce each other in the learning process, resulting in a very robust and discriminative dictionary. Moreover, to obtain class-wise separation of coefficient vectors from different classes, a support vector based classifier is integrated into the objective function. Finally, an iterative algorithm is devised to solve the proposed method efficiently. Experimental results illustrate that the optimal coding coefficients derived by CW-DDL are very effective for pattern classification. The proposed method shows the superior performance to related DDL methods on several benchmark datasets, and coupled with the CNN features, it also leads to the state-of-art performance on the more challenging dataset.

Fisher Discrimination Regularized Robust Coding Based on a Local Center for Tumor Classification

Tumor classification is crucial to the clinical diagnosis and proper treatment of cancers. In recent years, sparse representation-based classifier (SRC) has been proposed for tumor classification. The employed dictionary plays an important role in sparse representation-based or sparse coding-based classification. However, sparse representation-based tumor classification models have not used the employed dictionary, thereby limiting their performance. Furthermore, this sparse representation model assumes that the coding residual follows a Gaussian or Laplacian distribution, which may not effectively describe the coding residual in practical tumor classification. In the present study, we formulated a novel effective cancer classification technique, namely, Fisher discrimination regularized robust coding (FDRRC), by combining the Fisher discrimination dictionary learning method with the regularized robust coding (RRC) model, which searches for a maximum a posteriori solution to coding problems by assuming that the coding residual and representation coefficient are independent and identically distributed. The proposed FDRRC model is extensively evaluated on various tumor datasets and shows superior performance compared with various state-of-the-art tumor classification methods in a variety of classification tasks.