Kernel Dictionary Learning Research Articles

Extreme Kernel Sparse Learning for Tactile Object Recognition.

Tactile sensors play very important role for robot perception in the dynamic or unknown environment. However, the tactile object recognition exhibits great challenges in practical scenarios. In this paper, we address this problem by developing an extreme kernel sparse learning methodology. This method combines the advantages of extreme learning machine and kernel sparse learning by simultaneously addressing the dictionary learning and the classifier design problems. Furthermore, to tackle the intrinsic difficulties which are introduced by the representer theorem, we develop a reduced kernel dictionary learning method by introducing row-sparsity constraint. A globally convergent algorithm is developed to solve the optimization problem and the theoretical proof is provided. Finally, we perform extensive experimental validations on some public available tactile sequence datasets and show the advantages of the proposed method.

Kernel dictionary learning based discriminant analysis

Sparse representation based classification (SRC) has been successfully applied in many applications. But how to determine appropriate features that can best work with SRC remains an open question. Dictionary learning (DL) has played an import role in the success of sparse representation, while SRC treats the entire training set as a structured dictionary. In addition, as a linear algorithm, SRC cannot handle the data with highly nonlinear distribution. Motivated by these concerns, in this paper, we propose a novel feature learning method (termed kernel dictionary learning based discriminant analysis, KDL-DA). The proposed algorithm aims at learning a projection matrix and a kernel dictionary simultaneously such that in the reduced space the sparse representation of the data can be easily obtained, and the reconstruction residual can be further reduced. Thus, KDL-DA can achieve better performances in the projected space. Extensive experimental results show that our method outperforms many state-of-the-art methods.

Single image super-resolution via blind blurring estimation and dictionary learning

Learning-based methods have been becoming the mainstream of single image super resolution (SR) technologies. This kind of methods makes it effective to generate a high resolution (HR) image from a single low resolution (LR) image. There exists, however, two main problems with these methods: the quality of training data and the computational demand. We propose a novel framework for single image SR tasks in this paper, which consists of blur kernel estimation (BKE) and dictionary learning. BKE is utilized for improving the quality of training samples and realized by minimizing the dissimilarity between cross-scale patches iteratively. Couple dictionaries are trained by improved training samples before sparse recovery. More important is that a selective patch processing (SPP) strategy is adopted in BKE and sparse recovery, which brings more accurate BKE results and immensely reduces time consumption of the entire process. The experiments show that the proposed method produces more precise BKE estimation and better SR recovery than several typical SR algorithms at a higher efficiency.

ACCELERATING DYNAMIC MAGNETIC RESONANCE IMAGING BY NONLINEAR SPARSE CODING.

Although being high-dimensional, dynamic magnetic resonance images usually lie on low-dimensional manifolds. Nonlinear models have been shown to capture well that latent low-dimensional nature of data, and can thus lead to improvements in the quality of constrained recovery algorithms. This paper advocates a novel reconstruction algorithm for dynamic magnetic resonance imaging (dMRI) based on nonlinear dictionary learned from low-spatial but high-temporal resolution images. The nonlinear dictionary is initially learned using kernel dictionary learning, and the proposed algorithm subsequently alternates between sparsity enforcement in the feature space and the data-consistency constraint in the original input space. Extensive numerical tests demonstrate that the proposed scheme is superior to popular methods that use linear dictionaries learned from the same set of training data.

Linearized Kernel Dictionary Learning

In this paper we present a new approach of incorporating kernels into dictionary learning. The kernel K-SVD algorithm (KKSVD), which has been introduced recently, shows an improvement in classification performance, with relation to its linear counterpart K-SVD. However, this algorithm requires the storage and handling of a very large kernel matrix, which leads to high computational cost, while also limiting its use to setups with small number of training examples. We address these problems by combining two ideas: first we approximate the kernel matrix using a cleverly sampled subset of its columns using the Nystr\"{o}m method; secondly, as we wish to avoid using this matrix altogether, we decompose it by SVD to form new "virtual samples," on which any linear dictionary learning can be employed. Our method, termed "Linearized Kernel Dictionary Learning" (LKDL) can be seamlessly applied as a pre-processing stage on top of any efficient off-the-shelf dictionary learning scheme, effectively "kernelizing" it. We demonstrate the effectiveness of our method on several tasks of both supervised and unsupervised classification and show the efficiency of the proposed scheme, its easy integration and performance boosting properties.

Classification of LV wall motion in cardiac MRI using kernel Dictionary Learning with a parametric approach.

In this paper, we propose a parametric approach for the assessment of wall motion in Left Ventricle (LV) function in cardiac cine-Magnetic Resonance Imaging (MRI). Time-signal intensity curves (TSICs) are identified in Spatio-temporal image profiles extracted from different anatomical segments in a cardiac MRI sequence. Different parameters are constructed from specific TSICs that present a decreasing then increasing shape reflecting dynamic information of the LV contraction. The parameters extracted from these curves are related to: 1) an average curve based on a clustering process, 2) curve skewness and 3) cross correlation values between each average clustered curve and a patient-specific reference. Several tests are performed in order to construct different vectors to train a sparse classifier based on kernel Dictionary Learning (DL). Results are compared with other classifiers like Support Vector Machine (SVM) and Discriminative Dictionary Learning. The best classification performance is obtained with information of skewness and the average curve with an accuracy about 94% using the mentioned sparse based kernel DL with a radial basis function kernel.

Brain tumor image segmentation using kernel dictionary learning.

Automated brain tumor image segmentation with high accuracy and reproducibility holds a big potential to enhance the current clinical practice. Dictionary learning (DL) techniques have been applied successfully to various image processing tasks recently. In this work, kernel extensions of the DL approach are adopted. Both reconstructive and discriminative versions of the kernel DL technique are considered, which can efficiently incorporate multi-modal nonlinear feature mappings based on the kernel trick. Our novel discriminative kernel DL formulation allows joint learning of a task-driven kernel-based dictionary and a linear classifier using a K-SVD-type algorithm. The proposed approaches were tested using real brain magnetic resonance (MR) images of patients with high-grade glioma. The obtained preliminary performances are competitive with the state of the art. The discriminative kernel DL approach is seen to reduce computational burden without much sacrifice in performance.