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

Robust discriminative nonnegative dictionary learning for occluded face recognition

Synthesis linear classifier based analysis dictionary learning for pattern classification

We address the problem of sparse channel representation for downlink channel estimation in multi-user frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. Existing methods typically adopt discrete Fourier transform (DFT) matrix as a sparse basis to represent sparse channels. However, a sparse basis constructed through dictionary learning method has proven to have strong sparse channel representation. In this work, we develop a discriminative dictionary learning-based sparse channel representation for downlink channel estimation in a multi-user FDD massive MIMO systems. Considering partially shared support between near users, we present a new discriminative dictionary learning (DDL) method for sparse channel representation, based on which the channel estimation scheme is developed. Compared with learning a shared dictionary for all users, it can provide a better representation, thus improving the performance of recovery in the compressive sensing process. Numerical results demonstrate the superior performance of discriminative dictionary as compared to the shared dictionary in terms of normalized mean square error (NMSE) and symbol error rate (SER).

In this paper, a novel discriminative dictionary learning method is proposed for sparse-representation-based classification (SRC) to label highly dimensional hyperspectral imagery (HSI). In SRC, a dictionary is conventionally constructed using all of the training pixels, which is not only inefficient due to the large size of typical HSI images but also ineffective in capturing class-discriminative information crucial for classification. We address the dictionary design problem with the inspiration from the learning vector quantization technique and propose a hinge loss function that is directly related to the classification task as the objective function for dictionary learning. The resulting online learning procedure systematically “pulls” and “pushes” dictionary atoms so that they become better adapted to distinguish between different classes. In addition, the spatial context for a test pixel within its local neighborhood is modeled using a Bayesian graph model and is incorporated with the sparse representation of a single test pixel in a unified probabilistic framework, which enables further refinement of our dictionary to capture the spatial class dependence that complements the spectral information. Experiments on different HSI images demonstrate that the dictionaries optimized using our method can achieve higher classification accuracy with substantially reduced dictionary size than using the whole training set. The proposed method also outperforms existing dictionary learning methods and attains the state-of-the-art results in both the spectral-only and spatial-spectral settings.

Structured sparse representation has been recently found to achieve better efficiency and robustness in exploiting the target appearance model in tracking systems with both holistic and local information. Therefore, to better simultaneously discriminate multi-targets from their background, we propose a novel video-based multi-target tracking system that combines the particle probability hypothesis density (PHD) filter with discriminative group-structured dictionary learning. The discriminative dictionary with group structure learned by the hierarchical K-means clustering algorithm implicitly associates the dictionary atoms with the group labels, simultaneously enforcing the target candidates from the same group (class) to share the same structured sparsity pattern. Furthermore, we propose a new joint likelihood calculation by relating the discriminative sparse codes with the maximum voting technique to enhance the particle PHD updating step. Experimental results on two publicly available benchmark video sequences confirm the improved performance of our proposed method over other state-of-the-art techniques in video-based multi-target tracking.

Joint discriminative and shared dictionary learning with dictionary extension strategy for bearing fault classification

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.

Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmentation, which is more accurate than the three state-of-the-art MR prostate segmentation methods under comparison. For the second dataset, the MICCAI 2012 challenge dataset, the authors' proposed method yields a Dice Ratio of 87.4%, which also achieves better segmentation accuracy than other methods under comparison. A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable model and dictionary learning methods, which achieves more accurate segmentation performance on prostate T2 MR images.

An automatic segmentation method of hippo-campus for volume measurement in MR brain images by using sparse patch representation and discriminative dictionary learning is proposed in this paper, which can overcome the limitation of multi-atlas approaches that mostly rely on similarity between target image and atlases for more accurate segmentation. In the proposed method, atlases are registered to a target image, then patch-based features including intensity and gradient are extracted from registered atlases for a dictionary learning model, from which dictionaries and classifiers are learned simultaneously. The label for voxels in the target image is determined via solving sparse representation of target patch over learned dictionary. Performance of the proposed method is evaluated on 30 subjects from the public dataset CIND by cross validation. Qualitative and quantitative comparisons illustrate that the proposed method is competitive compared with state-of-the-art approaches based on multi-atlas.

Motivated by the successful application of discriminative dictionary learning, a new idea can be provided to the classifier for radar signal recognition. Based on the Fisher discriminative dictionary learning, a method of radar signal recognition is proposed by integrating feature extraction and dictionary learning. Firstly, 1/2 bispectra diagonal slice has been taken advantage of reducing the data dimension. Then, the sub-dictionary of each sort of signal can be trained by introducing Fisher criterion to make the dictionary more discriminative. At the same time, the reconstruction error and sparse coding coefficients are considered by discrimination criterion, so the classification of the objective function with reconstruction error and similarity of sparse coding coefficients has better discriminative performance. The simulation shows that comparing among the other four algorithms, the proposed method has more robust and the recognition rate improves 7.05% on average.

Wild animal detection is an active research area since last many decades among wildlife researchers to study and analyze wild animals and their behavior. This paper presents sparse representation based wild animal detection system using Discriminative Feature-oriented Dictionary Learning (DFDL). DFDL extracts discriminative class-specific features and shows a low complexity method for animal detection. We acquired class-specific dictionaries allowed to represent a new image to identity of the class of the image. Concurrently, these dictionaries are incapable of representing the samples of other classes. The experiments are performed over in-house database compiled by us. We achieved promising results using DFDL with 93% accuracy.

In deep dictionary learning multiple dictionaries are learned based on information at various levels of abstraction. We propose a novel hierarchical discriminative dictionary learning layer embedded within a neural network with an image classification objective. Discrimination is induced in the learned synthesis dictionaries at multiple hierarchical levels in a simple way by a one-hot-code representation of the class labels during the training backward pass. In addition, local sparse representation objectives are approximated during the forward pass, introducing local regularization. We evaluate our proposal on five known datasets and we either outperform state-of-the-art methods or achieve competitive classification results.

Although the object detection and recognition has received growing attention for decades, a robust fire and flame detection method is rarely explored. This paper presents a novel solid solution based on sparse representation with spatial pyramid pooling. Traditional sparse representation, as one of prevalent feature learning methods, is successfully applied for object detection. But it has some intrinsic defects. Firstly, it requires fixed input image size. Secondly, the accuracy of detection heavily depends on discriminative dictionary learning and feature coding. At last, it is usually very time-consuming. In this paper, we have proposed a novel dictionary learning method with the structure sparsity constraint to train a discriminative dictionary. In feature coding stage, we compute sparse codes of each patch with dictionaries learned from data and pool them to form local histogram in spatial pyramid manner. At lat, the feature vector is pipelined into a linear SVM classifier to train the model. For improving the efficiency, we also adopt the selective search approach to generate the candidate region proposals in the preprocessing stage. In processing test images, our method achieved better or comparable accuracy to the state-of-the-art on FlameDetection2010 Dataset.

Image set based classification (ISC) has attracted lots of research interest in recent years. Several ISC methods have been developed, and dictionary learning technique based methods obtain state-of-the-art performance. However, existing ISC methods usually transform the image sample of a set into a vector for subsequent processing, which breaks the inherent spatial structure of image sample and the set. In this paper, we utilize tensor to model an image set with two spatial modes and one set mode, which can fully explore the intrinsic structure of image set. We propose a novel ISC approach, named discriminant tensor dictionary learning with neighbor uncorrelation (DTDLNU), which jointly learns two spatial dictionaries and one set dictionary. The spatial and set dictionaries are composed by set-specific sub-dictionaries corresponding to the class labels, such that the reconstruction error is discriminative. To obtain dictionaries with favorable discriminative power, DTDLNU designs a neighbor-uncorrelated discriminant tensor dictionary term, which minimizes the within-class scatter of the training sets in the projected tensor space and reduces tensor dictionary correlation among set-specific sub-dictionaries corresponding to neighbor sets from different classes. Experiments on three challenging datasets demonstrate the effectiveness of DTDLNU.

Grid computing is employed to unravel massive computational problems by using large numbers of heterogeneous computers connected to the computing network. Job scheduling is an important part of the grid computing environment, which is employed to extend the throughput and reduce the turnaround and reaction time. This paper proposed a new scheduling algorithm called "Feed forward neural network in the grid computing (FFNNGC) system," which is used to solve some real-life problems related to the pattern classification. In the proposed method, we have used a feed-forward algorithm to find the output in the grid computing network, and the network training is done until the system converges to a minimum error solution. The pattern classification problem consists of 13 real-life, and artificial dataset problems, including two class and multiclass problems. Experiments were performed under these real-life problems, and the results indicated that the proposed method is helpful in such types of problems.