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

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.

Slip/antiblocking agents

This paper analyzes the role of dictionary selection in Sparse Representation-based Classification (SRC). While SRC introduces interesting results in the field of classification, its performance is highly limited by the number of training samples to form the classification matrix. Different studies addressed this issue by using a more compact representation of the training data in order to achieve higher classification speed and accuracy. Representative selection methods which are analyzed in this paper include Metaface dictionary learning, Fisher Discriminative Dictionary Learning (FDDL), Sparse Modeling Representative Selection (SMRS), and random selection of the training samples. The first two methods build their own dictionaries via an optimization process while the other two methods select the representatives directly from the original training samples. These methods, along with the original method which uses all training samples to form the classification matrix, were examined on two face datasets and one digit dataset. The role of feature extraction was also studied using two dimensionality reduction methods, down-sampling and random projection. The results show that the FDDL method leads to the best classification accuracy followed by the SMRS method as the second best. On the other hand, the SMRS method requires a much smaller learning time which makes it more appropriate for dynamic situations where the dictionary is regularly updated with new samples. The accuracy of the Metaface dictionary learning method was specifically less than the other two methods. As expected, using all the training samples as the dictionary resulted in the best recognition rates in all the datasets but the classification times for this approach were far larger than the required time using any of the three dictionary learning methods.

Recently, the projective dictionary pair learning (DPL) method has obtained better classification representation accuracy which learned a synthesis dictionary and an analysis dictionary to achieve the goal of signal representation. In order to obtain more discriminative ability of the dictionary pair, a new method based on DPL, called discrimination projective dictionary pair learning based dictionary learning method (DPDPL), will be proposed. In DPDPL, we will consider both the inter-class and intra-class incoherence constraints of the synthesis dictionary, and the analysis dictionary should be used to simultaneously maximize the total scatter and the between-class scatter of the signal after coding. Thus, the method ensures that the dictionary has better discriminative ability and the signals are more separable after coding. Experiments of sparse representation-based classification on several face databases show the good performances of the proposed dictionary learning method.

In this paper, we propose a simultaneous feature and dictionary learning (SFDL) method for image set based face recognition, where each training and testing example contains a face image set captured from different poses, illuminations, expressions and resolutions. While several feature learning and dictionary learning methods have been proposed for image set based face recognition in recent years, most of them learn the features and dictionaries separately, which may not be powerful enough because some discriminative information for dictionary learning may be compromised in the feature learning stage if they are applied sequentially, and vice versa. To address this, we propose a SFDL method to learn discriminative features and dictionaries simultaneously from raw face images so that discriminative information can be jointly exploited. Extensive experimental results on four widely used face datasets show that our method achieves better performance than state-of-the-art image set based face recognition methods.KeywordsFace recognitionimage setfeature learningdictionary learningsimultaneous learning

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).

Sparse representation and dictionary learning have been successfully applied to encode dense data and facilitate image classification. Though existing dictionary learning methods achieve better performance than their counterparts, the class discriminative ability of learned dictionary is still limited. This paper proposes a novel supervised dictionary learning method based on the prior of the block diagonal phenomenon, i.e., each sample should be well reconstructed by the samples in the same class while poorly reconstructed by the samples in other class. Specifically, a block diagonal regularizer is imposed on the affinity matrix to enforce the sparse representation matrix to have an approximately block diagonal structure, which makes the learned dictionary more discriminative and suitable for classification tasks. Furthermore, we present an effective optimization strategy by combining the alternating minimization with the alternating direction method of multipliers (ADMM) for the proposed framework. Experimental results on six real-world datasets show that the proposed method is more effective than state-of-the-art dictionary learning methods.

Bayesian dictionary learning for hyperspectral image super resolution in mixed Poisson–Gaussian noise

By finding broader temporal and spatial patterns of brain activity, dictionary learning and sparse coding techniques circumvent the constraints of typical voxel-level studies of task-based functional magnetic resonance imaging (fMRI) data. Prior applications of these algorithms to task-related fMRI data, on the other hand, have not been tuned to simultaneously uncover temporal patterns of activity that change in tandem with changes in task circumstances and spatial patterns that exploit existing neuroscience knowledge. This work, presents a sparse dictionary learning method that decomposes fMRI data into temporal patterns of signals that loosely differ between task conditions and sparse spatial patterns that are at least partially similar to known functional network hubs, based on prior knowledge of the temporal pattern of task conditions and the locations of brain regions hypothesized to be involved in the task. To over come the a major drawback of the prior work in terms being constrained to provide an equal number of spatial and temporal patterns, even when the data was more accurately represented by a larger number spatial than temporal patterns (or vice versa), this work also propose a constrained decoupled dictionary learning method that uses prior knowledge related to task paradigms and the spatial locations of task-related brain regions to estimate decoupled spatial and temporal patterns that represent the fMRI data. The utility of sparse dictionary learning methods applied to task fMRI data is increased by simultaneously exploiting the task's known temporal structure and biasing solutions towards putative network hubs. The method identifies a high percentage of the spatial and temporal patterns programmed into simulated fMRI data. The temporal and spatial patterns are identified using an effective on-line optimization methodology. The technique detects geographical and temporal patterns in synthetic task fMRI data. When applied to two real-world fMRI data sets, the method: 1. automatically identifies temporal and spatial patterns that were known by neuroscientists to be task-related a priori but were not provided as inputs to the method; 2. provides measurements that differ significantly between differing tasks. When applied to real fMRI data from 100 participants two different studies, the suggested method also identifies geographic regions known a priori to be engaged by the Attention Network Task (ANT), AX-Continuous Performance Task (AX-CPT) and Stroop Task more thoroughly than competing methods. Using task information to partially constrain dictionary learning allowed this method to identify task related networks in real and synthetic fMRI data.

Most supervised dictionary learning methods optimize the combinations of reconstruction error, sparsity prior, and discriminative terms. Thus, the learnt dictionaries may not be optimal for recognition tasks. Also, the sparse codes learning models in the training and the testing phases are inconsistent. Besides, without utilizing the intrinsic data structure, many dictionary learning methods only employ the l0 or l1 norm to encode each datum independently, limiting the performance of the learnt dictionaries. We present a novel bilevel model-based discriminative dictionary learning method for recognition tasks. The upper level directly minimizes the classification error, while the lower level uses the sparsity term and the Laplacian term to characterize the intrinsic data structure. The lower level is subordinate to the upper level. Therefore, our model achieves an overall optimality for recognition in that the learnt dictionary is directly tailored for recognition. Moreover, the sparse codes learning models in the training and the testing phases can be the same. We further propose a novel method to solve our bilevel optimization problem. It first replaces the lower level with its Karush-Kuhn-Tucker conditions and then applies the alternating direction method of multipliers to solve the equivalent problem. Extensive experiments demonstrate the effectiveness and robustness of our method.

In this paper, we focus on sparse dictionary learning that is widely used as a data processing technique in many real-world applications. Based on the frame of alternating direction method of multiplier (ADMM), we extend to an alternating direction and projection method for sparse dictionary learning. By introducing proximal mapping and the equivalence to the corresponding projection, a partial convergence result of this multi-block and nonconvex ADMM algorithm is given that the algorithm converges to a Karush-Kuhn-Tucker point whenever it converges.

Dictionary learning (DL) techniques aim to find sparse signal representations that capture prominent characteristics in a given data. Such methods operate on a data matrix $Y\in \mathbb {R}^{N\times M}$ , where each of its columns $y_i\in \mathbb {R}^N$ constitutes a training sample, and these columns together represent a sampling from the data manifold. For signals $y\in \mathbb {R}^N$ residing on weighted graphs, an additional challenge is incorporating the underlying geometric structure of the data domain into the learning process. In such cases, the topological graph structure may provide a crucial interpretation for the columns, while the data manifold itself may also possess a low-dimensional intrinsic structure that should be taken into account. In this work, we propose a novel dictionary learning algorithm for graph signals that simultaneously takes into account the underlying structure in both the signal and the manifold domains. Specifically, we require that the dictionary atoms are smooth with respect to the graph topology, as encapsulated by the graph Laplacian matrix. Furthermore, we propose to learn this graph Laplacian within the dictionary learning process, adapting it to promote the desired smoothness. Utilizing the manifold structure, we propose to encourage the smoothness of the sparse representations on the data manifold in a similar manner. Both these smoothness forces implicitly enhance the learned dictionary. The efficiency of the proposed approach is demonstrated on synthetic examples as well as on real data, showing that it outperforms other dictionary learning methods in typical problems such as resistance to noise and data completion.

Facial Expression (FE) detection is a popular research area, particularly in the field of Image Classification, Pattern Recognition and Computer Vision. Sparse Representation (SR) and Dictionary Learning (DL) have significantly enhanced the classification performance of image recognition and also resolved the problem of the nonlinear distribution of face images and its implementation with DL. However, the locality structure of face image data containing more discriminative information, which is very critical for classification has not been fully explored by state-of-the-art existing SR-based approaches. Furthermore, similar coding results between test samples and neighboring training data, contained in the feature space are not being fully realized from the image features with similar image categorizations, to effectively capture the embedded discriminative information. In an attempt to resolve the forgoing issues, we propose a novel DL method, Convolutional locality-sensitive Dictionary Learning (CLSDL) for Facial Expression detection.

Kernel-based Sparse Representation (SR) has impacted positively on the classification performance in image recognition and has eradicated the problems attributed to the nonlinear distribution of face images and its implementation with Dictionary Learning (DL). However, the locality construction of image data containing more discriminative information, which is crucial for classification has not been fully examined by the current Kernel Sparse representation-based approaches. Furthermore, similar coding outcomes between test samples and neighbouring training data, restrained in the kernel space are not being fully realized from the image features with similar image groupings to effectively capture the embedded discriminative information. To handle these issues, we propose a novel DL method, Kernel Locality-Sensitive Discriminative SR (K-LSDSR) for face recognition. In the proposed K-LSDSR, a discriminative loss function for the groupings based on sparse coefficients is introduced into a locality-sensitive DL (LSDL). After solving the optimized dictionary, the sparse coefficients for the testing image feature samples are obtained, and then the classification results for face recognition is realized by reducing the error between the original and reassembled samples. Experimental results have shown that the proposed K-LSDSR significantly improves the performance of face recognition accuracies compared with competing methods and is robust to various diverse environments in image recognition.

Person re-identification (re-id) is important for video surveillance, and has interestingly algorithm challenges and extensive practical applications. Recently, the Sparse Representation based Classification (SRC) produced excellent results in person re-identification, in which the Dictionary Learning (DL) method is a very important part. Discriminative power of the learned dictionary determines the performance of re-identification. Previous approaches usually discriminatively train the dictionary by enforcing explicit constraints on DL. In this paper, we propose a heuristic discriminative dictionary learning method which improves the discriminative power of dictionary by transforming the representation space of dictionary in training. First, we figure out the statistical distribution of the training data and divide the data into two categories. Second, a transformation function is used to change the dictionary’s expression space. The dictionary learned by our method is proved to be effective in person re-id. Experiments on the benchmark dataset (CAVIAR4REID, i-LIDS) demonstrate that the proposed method outperforms the state-of-the-art approaches.