Class-specific Sub-dictionaries Research Articles

Hierarchical Dictionary Learning for Vehicle Classification Based on the Carrier-Free UWB Radar

As a promising technique, dictionary learning (DL) for target recognition has seen a recent surge in recent years. Although many methods have been proposed to obtain discriminative dictionaries or coefficients via incorporating various constraints into the objective function, there are still two issues. First, it is well known that kinds of discriminative criteria on the objective function often involve substantial optimized items, increasing computation cost. Second, noises in the real world inevitably degrade the classification performance, while most DL algorithms disregard that. Aiming at these two problems, a hierarchical DL model is proposed for vehicle recognition based on the carrier-free ultrawideband (UWB) radar. With the purpose of successfully determining the identity of targets, we first learn several class-specific subdictionaries. Then, considering that the actual environment is filled with noises, we divided the learned dictionary atoms into signal and disturbance atoms in accordance with sparse coefficients to establish the signal dictionary and noise dictionary, respectively. Finally, the clean data are recovered over the corresponding signal dictionary, and meanwhile, the classification task is achieved. This hierarchical DL method takes into account both the noise-robust ability and discriminative power of the learned dictionary, in which the “atom selection” mechanism dramatically speeds up calculations. What is more, rather than imposing discriminative restraints on the objective function, we improve the K-SVD-based optimization process to complete hierarchical DL. Experimental results on the measured and synthetic data corroborate the effectiveness of the proposed method even under low signal-to-noise ratio (SNR) values. Especially, to testify to the generalization ability of the proposed method, we evaluate our algorithm on a public synthetic aperture radar (SAR) dataset (MSTAR).

Auto-encoder based structured dictionary learning for visual classification

Dictionary learning and deep learning can be combined to boost the performance of classification tasks. However, existing combined methods often learn multi-level dictionaries each of which is embedded in a network layer, involve a large number of parameters (elements of many dictionaries) and thus easily result in prohibitive computational cost and even overfitting. In this paper, we present a novel deep Auto-Encoder based Structured Dictionary (AESD) learning model, where we need to learn only one dictionary which is composed of class-specific sub-dictionaries, and supervision is introduced by imposing discriminative category constraints to empower the dictionary with discrimination. The encoding layers are designed with shared parameters which are exactly dependent on the dictionary carried by the decoding layer. This characterizes the learning process by forward-propagation based optimization w.r.t the dictionary only, leading to a light-weight network training. In addition to utilizing directly the trained encoding network combined with a minimum-reconstruction-residual scheme for single image based classification, to expand the application spectrum of our method, in the testing phase, we extend the proposed prototype into a Convolutional Encoder based Block Sparse Representation (CEBSR) model to promote the latent block sparsity in the joint representation of an image set, achieving improved image set based classification. Extensive experiments verify the performance of the learned dictionary for image classification, and the superiority of our extended model over the state-of-the-art image set classification methods.

JCS: An Explainable Surface Defects Detection Method for Steel Sheet by Joint Classification and Segmentation

For surface defect images that captured from a practical steel production line, different shape, size, location and texture of defect object may cause inter-class similarity and intra-class difference of defect images. Despite attractive results have been achieved in some surface methods for defect classification and segmentation, it is still far from meeting the needs of real-world applications due to lack of adaptiveness of these methods. Considering the surface defect image can be decomposed into defect foreground image and defect-free background image, the paper develops a novel joint classification and segmentation (JCS) approach to perform surface defects detection for steel sheet. It comprises of the classification method based on a class-specific and shared discriminative dictionary learning (CASDDL) and the segmentation method based on a double low-rank based matrix decomposition (DLMD), respectively. For the proposed CASDDL method, we learn a shared sub-dictionary as well as several class-specific sub-dictionaries to explicitly capture common information shared by all classes and class-specific information belonging to corresponding class. We adopt a mutual incoherence constrain for each sub-dictionary, a Fisher-like discriminative criterion and low-rank constrain on coding vector to improve the discriminative ability of learned dictionary. For the proposed DLMD method, we formulate the segmentation task as a double low-rank based matrix factorization problem, and the Laplacian and sparse regularization terms are introduced into the matrix decomposition framework. Experimental results demonstrate that our proposed JCS method achieve a comparable or better performance than the state-of- the-art methods in classifying and segmenting surface defects of steel sheet.

Double Graph Regularized Double Dictionary Learning for Image Classification

In this paper, we present a novel double graph regularized double dictionary learning (DGRDDL) method for image classification. The proposed method jointly constructs a number of class-specific sub-dictionaries to capture the most discriminative features (class-specific information) of each class, and a class-shared dictionary to model the common patterns (class-shared information) shared by the images from different classes. A novel double graph regularization is proposed to correctly represent and differentiate these two types of information. Specifically, an intra-class similarity graph constraint is imposed on the representation coefficients over the class-specific dictionaries, and an inter-class similarity graph constraint is applied on the representation coefficients over the class-shared dictionary. In this way, the representations learned by the proposed DGRDDL method can correctly model the local similarity relationships of the class-specific and the class-shared information in images, respectively. Moreover, due to the differences between the intra-class and inter-class similarity graphs, the two types of information can be appropriately separated and captured by the learned dictionaries. We evaluate the performance of the proposed method on six public datasets and compared against those of seven benchmark methods. The experimental results demonstrate the effectiveness and superiority of the proposed method in image classification over the benchmark dictionary learning methods.

Structured dictionary learning using mixed-norms and group-sparsity constraint

Recently, sparse representation and dictionary learning have shown significant performance in machine vision. In particular, several supervised dictionary learning methods have been proposed for classification aim and increasing its accuracy. Among them, structured dictionary learning is an interesting approach which captures the discriminative properties of each class and common features among all classes in class-specific sub-dictionaries and a distinct shared sub-dictionary, respectively. It extracts the structural information that exists in samples of each class to increase the classification accuracy. Therefore, in this paper, a group-based structured dictionary learning method is proposed that captures structural information in each class and learns class-specific and shared sub-dictionaries based on mixed $$l_{2,1}$$ norm. Also, mixed $$l_{2,1}$$ norm is used for acquiring the sparse coefficients of data samples based on the learned sub-dictionaries. Then, classification is done by finding the class with (1) minimum reconstruction error or (2) maximum number of nonzero groups based on $$l_{1,0}$$ norm. The proposed method is evaluated by conducting experiments on Extended YaleB, AR and CMU-PIE face databases and the USPS handwritten digits database. The experimental results demonstrate the effectiveness of the proposed method in data representation and classification.

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.

Sparse representation for image classification via paired dictionary learning

Sparse coding technique is usually applied for feature representation. To learn discriminative features for visual recognition, a dictionary learning method, called Paired Discriminative K-SVD (PD-KSVD), is presented in this paper. Firstly, to reduce the reconstruction error of positive class while increasing the errors of negative classes, the scheme inverted signal is applied to the negative training samples. Then, the class-specific sub-dictionaries are learned from pairs of positive and negative classes to jointly achieve high discrimination and low reconstruction errors for sparse coding. Multiple sub-dictionaries are concatenated with respect to the same negative class so that the non-zero sparse coefficients can be discriminatively distributed to improve classification accuracy. Last, sparse coefficients are solved via the concatenated sub-dictionaries and used to train the classifier. Compared to the existing dictionary learning methods, PD-KSVD method achieves superior performance in a variety of visual recognition tasks on several publicly available datasets.

Linearized kernel dictionary learning with group sparse priors for action recognition

Classification-driven dictionary has been successfully used in pattern recognition and computer vision in recent years. In this paper, a discriminative dictionary is constructed by concatenating all class specific sub-dictionaries and one sub-dictionary containing the common patterns. To further enhance the discriminative power, we also propose to use group sparse priors in the coding stage of the dictionary learning process. A kernel dictionary is learned to solve the same direction distribution problem existing in the traditional dictionary learning framework. Actually, the kernel dictionary is learned in a linearized manner by using virtual features. We evaluate our method on three public action datasets including facial expression, Hand Gesture and UCF Sports. Experimental results demonstrate that our method can achieve the better or at least competitive performance when compared with other action recognition methods.

Low-rank double dictionary learning from corrupted data for robust image classification

In this paper, we propose a novel low-rank double dictionary learning (LRD2L) method for robust image classification tasks, in which the training and testing samples are both corrupted. Unlike traditional dictionary learning methods, LRD2L simultaneously learns three components from corrupted training data: 1) a low-rank class-specific sub-dictionary for each class to capture the most discriminative class-specific features of each class, 2) a low-rank class-shared dictionary which models the common patterns shared in the data of different classes, and 3) a sparse error term to model the noise in data. Through low-rank class-shared dictionary and noise term, the proposed method can effectively separate the corruptions and noise in training samples from creating low-rank class-specific sub-dictionaries, which are employed for correctly reconstructing and classifying testing images. Comparative experiments are conducted on three public available databases. Experimental results are encouraging, demonstrating the effectiveness of the proposed method and its superiority in performance over the state-of-the-art dictionary learning 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.

Robust block sparse discriminative classification framework.

In this paper, a block sparse discriminative classification framework (BSDC) is proposed under the assumption that a block or group structure exists in sparse coefficients on classification. First, we propose a block discriminative dictionary-learning (BDDL) algorithm, which learns class-specific subdictionaries and forces the sparse coefficients to be block sparse. An efficient gradient-based optimization strategy of BDDL also is developed, and the block sparse constraint of the sparse coefficient leads to a least-squares solution of nonzero entries in the sparse coding stage of dictionary learning. Second, to take advantage of the structures when a new test sample is given, conventional sparse coding algorithms are discarded, and structured sparse coding methods are adopted. Experiments validate the effectiveness of the proposed framework in face recognition and texture classification. We also show that BSDC is robust to noise.

A NOVEL SUPERVISED STRUCTURE DICTIONARY LEARNING FOR CLASSIFICATION BASED ON SPARSE REPRESENTATION

Sparse representation based classification has led to interesting image recognition results, while the dictionary used for sparse coding plays a key role in it. This paper presents a novel supervised structure dictionary learning (SSDL) algorithm to learn a discriminative and block structure dictionary. We associate label information with each dictionary item and make each class-specific sub-dictionary in the whole structured dictionary have good representation ability to the training samples from the associated class. More specifically, we learn a structured dictionary and a multiclass classifier simultaneously. Adding an inhomogeneous representation term to the objective function and considering the independence of the class-specific sub-dictionaries improve the discrimination capabilities of the sparse coordinates. An iteratively optimization method be proposed to solving the new formulation. Experimental results on four face databases demonstrate that our algorithm outperforms recently proposed competing sparse coding methods.