Dictionary Learning Framework Research Articles

Learning Based Segmentation of CT Brain Images: Application to Postoperative Hydrocephalic Scans.

Hydrocephalus is a medical condition in which there is an abnormal accumulation of cerebrospinal fluid (CSF) in the brain. Segmentation of brain imagery into brain tissue and CSF [before and after surgery, i.e., preoperative (pre-op) versus postoperative (post-op)] plays a crucial role in evaluating surgical treatment. Segmentation of pre-op images is often a relatively straightforward problem and has been well researched. However, segmenting post-op computational tomographic (CT) scans becomes more challenging due to distorted anatomy and subdural hematoma collections pressing on the brain. Most intensity- and feature-based segmentation methods fail to separate subdurals from brain and CSF as subdural geometry varies greatly across different patients and their intensity varies with time. We combat this problem by a learning approach that treats segmentation as supervised classification at the pixel level, i.e., a training set of CT scans with labeled pixel identities is employed. Our contributions include: 1) a dictionary learning framework that learns class (segment) specific dictionaries that can efficiently represent test samples from the same class while poorly represent corresponding samples from other classes; 2) quantification of associated computation and memory footprint; and 3) a customized training and test procedure for segmenting post-op hydrocephalic CT images. Experiments performed on infant CT brain images acquired from the CURE Children's Hospital of Uganda reveal the success of our method against the state-of-the-art alternatives. We also demonstrate that the proposed algorithm is computationally less burdensome and exhibits a graceful degradation against a number of training samples, enhancing its deployment potential.

Exploiting Spatial-Temporal Locality of Tracking via Structured Dictionary Learning.

In this paper, a novel spatial-temporal locality is proposed and unified via a discriminative dictionary learning framework for visual tracking. By exploring the strong local correlations between temporally obtained target and their spatially distributed nearby background neighbors, a spatial-temporal locality is obtained. The locality is formulated as a subspace model and exploited under a unified structure of discriminative dictionary learning with a subspace structure. Using the learned dictionary, the target and its background can be described and distinguished effectively through their sparse codes. As a result, the target is localized by integrating both the descriptive and the discriminative qualities. Extensive experiments on various challenging video sequences demonstrate the superior performance of proposed algorithm over the other state-of-the-art approaches.

Synthesis K-SVD based analysis dictionary learning for pattern classification

In the fields of computer vision and pattern recognition, dictionary learning techniques have been widely applied. In classification tasks, synthesis dictionary learning is usually time-consuming during the classification stage because of the sparse reconstruction procedure. Analysis dictionary learning, which is another research line, is more favorable due to its flexible representative ability and low classification complexity. In this paper, we propose a novel discriminative analysis dictionary learning method to enhance classification performance. Particularly, we incorporate a linear classifier and the supervised information into the traditional analysis dictionary learning framework by adding a discrimination error term. A synthesis K-SVD based algorithm which can effectively constrain the sparsity is presented to solve the proposed model. Extensive comparison experiments on benchmark databases validate the satisfactory performance of our method.

Landmark-Based Shape Encoding and Sparse-Dictionary Learning in the Continuous Domain.

We provide a generic framework to learn shape dictionaries of landmark-based curves that are defined in the continuous domain. We first present an unbiased alignment method that involves the construction of a mean shape as well as training sets whose elements are subspaces that contain all affine transformations of the training samples. The alignment relies on orthogonal projection operators that have a closed form. We then present algorithms to learn shape dictionaries according to the structure of the data that needs to be encoded: 1) projection-based functional principal-component analysis for homogeneous data and 2) continuous-domain sparse shape encoding to learn dictionaries that contain imbalanced data, outliers, or different types of shape structures. Through parametric spline curves, we provide a detailed and exact implementation of our method. We demonstrate that it requires fewer parameters than purely discrete methods and that it is computationally more efficient and accurate. We illustrate the use of our framework for dictionary learning of structures in biomedical images as well as for shape analysis in bioimaging.

Discriminative Structured Dictionary Learning on Grassmann Manifolds and Its Application on Image Restoration.

Image restoration is a difficult and challenging problem in various imaging applications. However, despite of the benefits of a single overcomplete dictionary, there are still several challenges for capturing the geometric structure of image of interest. To more accurately represent the local structures of the underlying signals, we propose a new problem formulation for sparse representation with block-orthogonal constraint. There are three contributions. First, a framework for discriminative structured dictionary learning is proposed, which leads to a smooth manifold structure and quotient search spaces. Second, an alternating minimization scheme is proposed after taking both the cost function and the constraints into account. This is achieved by iteratively alternating between updating the block structure of the dictionary defined on Grassmann manifold and sparsifying the dictionary atoms automatically. Third, Riemannian conjugate gradient is considered to track local subspaces efficiently with a convergence guarantee. Extensive experiments on various datasets demonstrate that the proposed method outperforms the state-of-the-art methods on the removal of mixed Gaussian-impulse noise.

Robust dictionary learning with graph regularization for unsupervised person re-identification

Most existing approaches for person re-identification are designed in a supervised way, undergoing a prohibitively high labeling cost and poor scalability. Besides establishing effective similarity distance metrics, these supervised methods usually focus on constructing discriminative and robust features, which is extremely difficult due to the significant viewpoint variations. To overcome these challenges, we propose a novel unsupervised method, termed as Robust Dictionary Learning with Graph Regularization (RDLGR), which can guarantee view-invariance through learning a dictionary shared by all the camera views. To avoid the significant degradation of performance caused by outliers, we employ a capped l 2,1-norm based loss to make our model more robust, addressing the problem that traditional quadratic loss is known to be easily dominated by outliers. Considering the lack of labeled cross-view discriminative information in our unsupervised method, we further introduce a cross-view graph Laplacian regularization term into the framework of dictionary learning. As a result, the geographical structure of original data space can be preserved in the learned latent subspace as discriminative information, making it possible to further boost the matching accuracy. Extensive experimental results over four widely used benchmark datasets demonstrate the superiority of the proposed model over the state-of-the-art methods.

Specificity and Latent Correlation Learning for Action Recognition Using Synthetic Multi-View Data From Depth Maps.

This paper presents a novel approach to action recognition using synthetic multi-view data from depth maps. Specifically, multiple views are first generated by rotating 3D point clouds from depth maps. A pyramid multi-view depth motion template is then adopted for multi-view action representation, characterizing the multi-scale motion and shape patterns in 3D. Empirically, despite the view-specific information, the latent information between multiple views often provides important cues for action recognition. Concentrating on this observation and motivated by the success of the dictionary learning framework, this paper proposes to explicitly learn a view-specific dictionary (called specificity) for each view, and simultaneously learn a latent dictionary (called latent correlation) across multiple views. Thus, a novel method, specificity and latent correlation learning, is put forward to learn the specificity that captures the most discriminative features of each view, and learn the latent correlation that contributes the inherent 3D information to multiple views. In this way, a compact and discriminative dictionary is constructed by specificity and latent correlation for feature representation of actions. The proposed method is evaluated on the MSR Action3D, the MSR Gesture3D, the MSR Action Pairs, and the ChaLearn multi-modal data sets, consistently achieving promising results compared with the state-of-the-art methods based on depth data.

Segment-Oriented Depiction and Analysis for Hyperspectral Image Data

A novel segment-oriented dictionary learning (SeODL) framework for hyperspectral image (HSI) classification is proposed. Differing from existing HSI classification methods which directly process the original whole spectral curves of pixels, our work focuses on local segment analysis to achieve fine depiction and effective exploitation. Viewing the separated segment as a basic processing unit, we first cluster them into two sets with the homogeneity in trend and fluctuation, and then two small dictionaries can be quickly learned. Second, to get meticulous and discriminability enhanced segment-oriented representations (SORs), the segments of the training and test pixels are coded on a novel binary-separated coding strategy. The coding stage for obtaining SORs is sped up by the employment of our proposed enhanced orthogonal matching pursuit technique. A characteristic splicing classifier with high performance can be trained using these SORs of the training pixels. Finally, a spiral searching strategy and a multiple majority-voting method are adopted for fully spatial information incorporation of the test pixels whose final SORs will be embedded into the trained characteristics splicing classifier to ascertain the labels. Experimental results on three real HSI data sets demonstrate the superiority of the proposed SeODL framework over several well-known classification algorithms in terms of classification accuracies.

Joint dictionary and metric learning for person re-identification

Matching people across non-overlapping camera views, known as person re-identification, is of great importance for long-term pedestrian tracking in smart surveillance systems. Among various algorithms for person re-identification, dictionary learning is frequently utilized to build robust feature representation for images across different camera views. Metric learning, on the other hand, is usually exploited to find optimal feature subspace that maximizes the inter-person divergence while minimizes the intra-person divergence. Although both representative features and discriminative metrics have great impacts on the performance of person re-identification methods, most of the existing algorithms focus on only one of the two aspects. In this paper, by explicitly modeling discriminative metric learning into the dictionary learning procedure, we propose to formulate robust feature representation learning and discriminative metric learning into a unified framework. To alleviate the issue of amount bias towards hard negative pairs in metric learning, instance selection is conducted for hard negative mining during the similarity constraint formulation. Besides, we come up with closed-form solutions for dictionary and coefficients update. Extensive experiments on three challenging datasets as well as the cross datasets experiments demonstrate the effectiveness and generalization ability of the joint dictionary and metric learning framework.

Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification.

A linear synthesis model-based dictionary learning framework has achieved remarkable performances in image classification in the last decade. Behaved as a generative feature model, it, however, suffers from some intrinsic deficiencies. In this paper, we propose a novel parametric nonlinear analysis cosparse model (NACM) with which a unique feature vector will be much more efficiently extracted. Additionally, we derive a deep insight to demonstrate that NACM is capable of simultaneously learning the task-adapted feature transformation and regularization to encode our preferences, domain prior knowledge, and task-oriented supervised information into the features. The proposed NACM is devoted to the classification task as a discriminative feature model and yield a novel discriminative nonlinear analysis operator learning framework (DNAOL). The theoretical analysis and experimental performances clearly demonstrate that DNAOL will not only achieve the better or at least competitive classification accuracies than the state-of-the-art algorithms, but it can also dramatically reduce the time complexities in both training and testing phases.

Compressed sampling and dictionary learning framework for wavelength-division-multiplexing-based distributed fiber sensing.

We propose a compressed sampling and dictionary learning framework for fiber-optic sensing using wavelength-tunable lasers. A redundant dictionary is generated from a model for the reflected sensor signal. Imperfect prior knowledge is considered in terms of uncertain local and global parameters. To estimate a sparse representation and the dictionary parameters, we present an alternating minimization algorithm that is equipped with a preprocessing routine to handle dictionary coherence. The support of the obtained sparse signal indicates the reflection delays, which can be used to measure impairments along the sensing fiber. The performance is evaluated by simulations and experimental data for a fiber sensor system with common core architecture.

Progressive Dictionary Learning With Hierarchical Predictive Structure for Low Bit-Rate Scalable Video Coding.

Dictionary learning has emerged as a promising alternative to the conventional hybrid coding framework. However, the rigid structure of sequential training and prediction degrades its performance in scalable video coding. This paper proposes a progressive dictionary learning framework with hierarchical predictive structure for scalable video coding, especially in low bitrate region. For pyramidal layers, sparse representation based on spatio-temporal dictionary is adopted to improve the coding efficiency of enhancement layers with a guarantee of reconstruction performance. The overcomplete dictionary is trained to adaptively capture local structures along motion trajectories as well as exploit the correlations between the neighboring layers of resolutions. Furthermore, progressive dictionary learning is developed to enable the scalability in temporal domain and restrict the error propagation in a closed-loop predictor. Under the hierarchical predictive structure, online learning is leveraged to guarantee the training and prediction performance with an improved convergence rate. To accommodate with the state-of-the-art scalable extension of H.264/AVC and latest High Efficiency Video Coding (HEVC), standardized codec cores are utilized to encode the base and enhancement layers. Experimental results show that the proposed method outperforms the latest scalable extension of HEVC and HEVC simulcast over extensive test sequences with various resolutions.

Task fMRI data analysis based on supervised stochastic coordinate coding.

Task functional magnetic resonance imaging (fMRI) has been widely employed for brain activation detection and brain network analysis. Modeling rich information from spatially-organized collection of fMRI time series is challenging because of the intrinsic complexity. Hypothesis-driven methods, such as the general linear model (GLM), which regress exterior stimulus from voxel-wise functional brain activity, are limited due to overlooking the complexity of brain activities and the diversity of concurrent brain networks. Recently, sparse representation and dictionary learning methods have attracted increasing interests in task fMRI data analysis. The major advantage of this methodology is its promise in reconstructing concurrent brain networks systematically. However, this data-driven strategy is, to some extent, arbitrary and does not sufficiently utilize the prior information of task design and neuroscience knowledge. To bridge this gap, we here propose a novel supervised sparse representation and dictionary learning framework based on stochastic coordinate coding (SCC) algorithm for task fMRI data analysis, in which certain brain networks are learned with known information such as pre-defined temporal patterns and spatial network patterns, and at the same time other networks are learned automatically from data. Our proposed method has been applied to two independent task fMRI datasets, and qualitative and quantitative evaluations have shown that our method provides a new and effective framework for task fMRI data analysis.

A Sparse Dictionary Learning Framework to Discover Discriminative Source Activations in EEG Brain Mapping

Electroencephalography (EEG) source analysis is one of the most important noninvasive human brain imaging tools that provides millisecond temporal accuracy. However, discovering essential activated brain sources associated with different brain status is still a challenging problem. In this study, we propose for the first time that the ill-posed EEG inverse problem can be formulated and solved as a sparse over-complete dictionary learning problem. In particular, a novel supervised sparse dictionary learning framework was developed for EEG source reconstruction. A revised version of discriminative K-SVD (DK-SVD) algorithm is exploited to solve the formulated supervised dictionary learning problem. As the proposed learning framework incorporated the EEG label information of different brain status, it is capable of learning a sparse representation that reveal the most discriminative brain activity sources among different brain states. Compared to the state-of-the-art EEG source analysis methods, proposed sparse dictionary learning framework achieved significant superior performance in both computing speed and accuracy for the challenging EEG source reconstruction problem through extensive numerical experiments. More importantly, the experimental results also validated that the proposed sparse learning framework is effective to discover the discriminative task-related brain activation sources, which shows the potential to advance the high resolution EEG source analysis for real-time non-invasive brain imaging research.

Online structured sparse learning with labeled information for robust object tracking

We formulate object tracking under the particle filter framework as a collaborative tracking problem. The priori information from training data is exploited effectively to online learn a discriminative and reconstructive dictionary, simultaneously without losing structural information. Specifically, the class label and the semantic structure information are incorporated into the dictionary learning process as the classification error term and ideal coding regularization term, respectively. Combined with the traditional reconstruction error, a unified dictionary learning framework for robust object tracking is constructed. By minimizing the unified objective function with different mixed norm constraints on sparse coefficients, two robust optimizing methods are developed to learn the high-quality dictionary and optimal classifier simultaneously. The best candidate is selected by minimizing the reconstructive error and classification error jointly. As the tracking continues, the proposed algorithms alternate between the robust sparse coding and the dictionary updating. The proposed trackers are empirically compared with 14 state-of-the-art trackers on some challenging video sequences. Both quantitative and qualitative comparisons demonstrate that the proposed algorithms perform well in terms of accuracy and robustness.

Large Inpainting of Face Images With Trainlets

Image inpainting is concerned with the completion of missing data in an image. When the area to inpaint is relatively large, this problem becomes challenging. In these cases, traditional methods based on patch models and image propagation are limited, since they fail to consider a global perspective of the problem. In this letter, we employ a recently proposed dictionary learning framework, coined Trainlets, to design large adaptable atoms from a corpus of various datasets of face images by leveraging the online sparse dictionary learning algorithm. We, therefore, formulate the inpainting task as an inverse problem with a sparse-promoting prior based on the learned global model. Our results show the effectiveness of our scheme, obtaining much more plausible results than competitive methods.

Learning universal multiview dictionary for human action recognition

Recently, many sparse coding based approaches have been proposed for human action recognition. However, most of them focus on learning a discriminative dictionary without explicitly taking into account the common patterns shared among different action classes. In this paper, we propose a novel discriminative dictionary learning framework by formulating a universal dictionary which consists of a shared sub-dictionary and a set of class-specific sub-dictionaries. As a result, inter-class differences can be better characterized with sparse codes obtained from the class-specific dictionaries. In addition, group sparsity and locality constraints are utilized to preserve the relationship and structure among features. In order to leverage the benefits of multiple descriptors, a dictionary is learned for each view, and the corresponding sparse representations of those descriptors are fused in a low dimensional feature space together with temporal information. The experimental results on three challenging datasets demonstrate that our method is able to achieve better performance than a number of state-of-the-art ones.

A Semi-Supervised Predictive Sparse Decomposition Based on Task-Driven Dictionary Learning

In feature learning field, many methods are inspired by advances in neuroscience. Among them, neural network and sparse coding have been broadly studied. Predictive sparse decomposition (PSD) is a practical variant of these two methods. It trains a neural network to estimate the sparse codes. After training, the neural network is fine-tuned to achieve higher performance on object recognition tasks. It is widely believed that introducing discriminative information can make the features more useful for classification task. Hence, in this work, we propose applying the task-driven dictionary learning framework to the PSD and demonstrate that this new model can be optimized by the stochastic gradient descent (SGD) algorithm. Before our work, the semi-supervised auto-encoder framework has already been proposed to guide neural network to extract discriminative representations. But it does not improve the classification performance of neural network. In the experiments, we compare the proposed method with the semi-supervised auto-encoder method. The performance of PSD is used as the baseline for these two methods. On the MNIST and USPS datasets, our method can generate more discriminative and predictable sparse codes than other methods. Furthermore, the recognition accuracy of neural network can be improved.

Fast Low-Rank Shared Dictionary Learning for Image Classification.

Despite the fact that different objects possess distinct class-specific features, they also usually share common patterns. This observation has been exploited partially in a recently proposed dictionary learning framework by separating the particularity and the commonality (COPAR). Inspired by this, we propose a novel method to explicitly and simultaneously learn a set of common patterns as well as class-specific features for classification with more intuitive constraints. Our dictionary learning framework is hence characterized by both a shared dictionary and particular (class-specific) dictionaries. For the shared dictionary, we enforce a low-rank constraint, i.e., claim that its spanning subspace should have low dimension and the coefficients corresponding to this dictionary should be similar. For the particular dictionaries, we impose on them the well-known constraints stated in the Fisher discrimination dictionary learning (FDDL). Furthermore, we develop new fast and accurate algorithms to solve the subproblems in the learning step, accelerating its convergence. The said algorithms could also be applied to FDDL and its extensions. The efficiencies of these algorithms are theoretically and experimentally verified by comparing their complexities and running time with those of other well-known dictionary learning methods. Experimental results on widely used image data sets establish the advantages of our method over the state-of-the-art dictionary learning methods.

Boosted Dictionary Learning for Image Compression

Sparse representations over redundant dictionaries have shown to produce high-quality results in various signal and image processing tasks. Recent advancements in learning-based dictionary design have made image compression using data-adaptive learned dictionaries a promising field. In this paper, we present a boosted dictionary learning framework to construct an ensemble of complementary specialized dictionaries for sparse image representation. Boosted dictionaries along with a competitive sparse coding form our ensemble model which can provide us with more efficient sparse representations. The constituent dictionaries of the ensemble are obtained using a coherence regularized dictionary learning model for which two novel dictionary optimization algorithms are proposed. These algorithms improve the generalization properties of the trained dictionary compared with several incoherent dictionary learning methods. Based on the proposed ensemble model, we then develop a new image compression algorithm using boosted multi-scale dictionaries learned in the wavelet domain. Our algorithm is evaluated for the compression of natural images. Experimental results demonstrate that the proposed algorithm has better rate–distortion performance as compared with several competing compression methods, including analytic and learned dictionary schemes.