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https://www.readbyqxmd.com/read/28214787/sparse-and-dense-hybrid-representation-via-subspace-modeling-for-dynamic-mri
#1
Qiegen Liu, Shanshan Wang, Dong Liang
Recent theoretical results on compressed sensing and low-rank matrix recovery have inspired significant interest in joint sparse and low rank modeling of dynamic magnetic resonance imaging (dMRI). Existing approaches usually describe these two respective prior information with different formulations. In this paper, we present a novel sparse and dense hybrid representation (SDR) model which describes the sparse plus low rank properties by a unified way. More specifically, under the learned dictionary consisting of temporal basis functions, SDR models the spatial coefficients in two subspaces with Laplacian and Gaussian prior distributions, respectively...
February 5, 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/28212079/multi-view-multi-instance-learning-based-on-joint-sparse-representation-and-multi-view-dictionary-learning
#2
Bing Li, Chunfeng Yuan, Weihua Xiong, Weiming Hu, Houwen Peng, Xinmiao Ding, Stephen Maybank
In multi-instance learning (MIL), the relations among instances in a bag convey important contextual information in many applications. Previous studies on MIL either ignore such relations or simply model them with a fixed graph structure so that the overall performance inevitably degrades in complex environments. To address this problem, this paper proposes a novel multi-view multi-instance learning algorithm (M2IL) that combines multiple context structures in a bag into a unified framework. The novel aspects are: (i) we propose a sparse "-graph model that can generate different graphs with different parameters to represent various context relations in a bag, (ii) we propose a multi-view joint sparse representation that integrates these graphs into a unified framework for bag classification, and (iii) we propose a multi-view dictionary learning algorithm to obtain a multi-view graph dictionary that considers cues from all views simultaneously to improve the discrimination of the M2IL...
February 14, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28187898/vessel-segmentation-and-microaneurysm-detection-using-discriminative-dictionary-learning-and-sparse-representation
#3
Malihe Javidi, Hamid-Reza Pourreza, Ahad Harati
Diabetic retinopathy (DR) is a major cause of visual impairment, and the analysis of retinal image can assist patients to take action earlier when it is more likely to be effective. The accurate segmentation of blood vessels in the retinal image can diagnose DR directly. In this paper, a novel scheme for blood vessel segmentation based on discriminative dictionary learning (DDL) and sparse representation has been proposed. The proposed system yields a strong representation which contains the semantic concept of the image...
February 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28182553/multi-dimensional-sparse-models
#4
Na Qi, Yunhui Shi, Xiaoyan Sun, Jingdong Wang, Baocai Yin, Junbin Gao
Traditional synthesis/analysis sparse representation models signals in a one dimensional (1D) way, in which a multidimensional (MD) signal is converted into a 1D vector. 1D modeling cannot sufficiently handle MD signals of high dimensionality in limited computational resources and memory usage, as breaking the data structure and inherently ignores the diversity of MD signals (tensors). We utilize the multilinearity of tensors to establish the redundant basis of the space of multi linear maps with the sparsity constraint, and further propose MD synthesis/analysis sparse models to effectively and efficiently represent MD signals in their original form...
February 2, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28129152/a-new-sparse-representation-framework-for-reconstruction-of-an-isotropic-high-spatial-resolution-mr-volume-from-orthogonal-anisotropic-resolution-scans
#5
Yuanyuan Jia, Ali Gholipour, Zhongshi He, Simon Warfield
In magnetic resonance (MR), hardware limitations, scan time constraints, and patient movement often result in the acquisition of anisotropic 3D MR images with limited spatial resolution in the out-of-plane views. Our goal is to construct an isotropic high-resolution 3D MR image through upsampling and fusion of orthogonal anisotropic input scans. We propose a multi-frame super-resolution (SR) reconstruction technique based on sparse representation of MR images. Our proposed algorithm exploits the correspondence between the high-resolution slices and the low-resolution sections of the orthogonal input scans as well as the self-similarity of each input scan to train pairs of over-complete dictionaries that are used in a sparse land local model to upsample the input scans...
January 23, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28114453/fully-automated-macular-pathology-detection-in-retina-optical-coherence-tomography-images-using-sparse-coding-and-dictionary-learning
#6
Yankui Sun, Shan Li, Zhongyang Sun
No abstract text is available yet for this article.
January 1, 2017: Journal of Biomedical Optics
https://www.readbyqxmd.com/read/28114018/modality-invariant-image-classification-based-on-modality-uniqueness-and-dictionary-learning
#7
Seungryong Kim, Rui Cai, Kihong Park, Sunok Kim, Kwanghoon Sohn
We present a unified framework for image classification of image sets taken under varying modality conditions. Our method is motivated by a key observation that the image feature distribution is simultaneously influenced by the semantic-class and the modality category label, which limits the performance of conventional methods for that task. With this insight, we introduce modality uniqueness as a discriminative weight that divides each modality cluster from all other clusters. By leveraging the modality uniqueness, our framework is formulated as unsupervised modality clustering and classifier learning based on modality-invariant similarity kernel...
December 2, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28114004/submodular-attribute-selection-for-visual-recognition
#8
Jingjing Zheng, Zhuolin Jiang, Rama Chellappa
In real-world visual recognition problems, low-level features cannot adequately characterize the semantic content in images, or the spatio-temporal structure in videos. In this work, we encode objects or actions based on attributes that describe them as high-level concepts. We consider two types of attributes. One type of attributes is generated by humans, while the second type is data-driven attributes extracted from data using dictionary learning methods. Attribute-based representation may exhibit variations due to noisy and redundant attributes...
December 7, 2016: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28113847/hyperspectral-image-super-resolution-via-non-negative-structured-sparse-representation
#9
Weisheng Dong, Fazuo Fu, Guangming Shi, Xun Cao, Jinjian Wu, Guangyu Li, Xin Li
Hyperspectral imaging has many applications from agriculture and astronomy to surveillance and mineralogy. However, it is often challenging to obtain High-resolution (HR) hyperspectral images using existing hyperspectral imaging techniques due to various hardware limitations. In this paper, we propose a new Hyperspectral image super-resolution method from a low-resolution (LR) image and a HR reference image of the same scene. The estimation of the HR hyperspectral image is formulated as a joint estimation of the hyperspectral dictionary and the sparse codes based on the prior knowledge of the spatialspectral sparsity of the hyperspectral image...
March 22, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28113811/cross-view-action-recognition-via-transferable-dictionary-learning
#10
Jingjing Zheng, Zhuolin Jiang, Rama Chellappa
Discriminative appearance features are effective for recognizing actions in a fixed view, but may not generalize well to a new view. In this paper, we present two effective approaches to learn dictionaries for robust action recognition across views. In the first approach, we learn a set of view-specific dictionaries where each dictionary corresponds to one camera view. These dictionaries are learned simultaneously from sets of correspondence videos taken at different views with the aim of encouraging each video in the set to have the same sparse representation...
March 29, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28113681/riemannian-dictionary-learning-and-sparse-coding-for-positive-definite-matrices
#11
Anoop Cherian, Suvrit Sra
Data encoded as symmetric positive definite (SPD) matrices frequently arise in many areas of computer vision and machine learning. While these matrices form an open subset of the Euclidean space of symmetric matrices, viewing them through the lens of non-Euclidean Riemannian (Riem) geometry often turns out to be better suited in capturing several desirable data properties. Inspired by the great success of dictionary learning and sparse coding (DLSC) for vector-valued data, our goal in this paper is to represent data in the form of SPD matrices as sparse conic combinations of SPD atoms from a learned dictionary via a Riem geometric approach...
September 13, 2016: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28113537/exploiting-attribute-correlations-a-novel-trace-lasso-based-weakly-supervised-dictionary-learning-method
#12
Lin Wu, Yang Wang, Shirui Pan
It is now well established that sparse representation models are working effectively for many visual recognition tasks, and have pushed forward the success of dictionary learning therein. Recent studies over dictionary learning focus on learning discriminative atoms instead of purely reconstructive ones. However, the existence of intraclass diversities (i.e., data objects within the same category but exhibit large visual dissimilarities), and interclass similarities (i.e., data objects from distinct classes but share much visual similarities), makes it challenging to learn effective recognition models...
October 4, 2016: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28113449/blessing-of-dimensionality-recovering-mixture-data-via-dictionary-pursuit
#13
Guangcan Liu, Qingshan Liu, Ping Li
This paper studies the problem of recovering the authentic samples that lie on a union of multiple subspaces from their corrupted observations. Due to the high-dimensional and massive nature of today's data-driven community, it is arguable that the target matrix (i.e., authentic sample matrix) to recover is often low-rank. In this case, the recently established Robust Principal Component Analysis (RPCA) method already provides us a convenient way to solve the problem of recovering mixture data. However, in general, RPCA is not good enough because the incoherent condition assumed by RPCA is not so consistent with the mixture structure of multiple subspaces...
March 9, 2016: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28113386/dynamically-modulated-mask-sparse-tracking
#14
Zijing Chen, Xinge You, Boxuan Zhong, Jun Li, Dacheng Tao
Visual tracking is a critical task in many computer vision applications such as surveillance and robotics. However, although the robustness to local corruptions has been improved, prevailing trackers are still sensitive to large scale corruptions, such as occlusions and illumination variations. In this paper, we propose a novel robust object tracking technique depends on subspace learning-based appearance model. Our contributions are twofold. First, mask templates produced by frame difference are introduced into our template dictionary...
September 7, 2016: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28113275/on-the-equivalence-of-the-lc-ksvd-and-the-d-ksvd-algorithms
#15
Igor Kviatkovsky, Moshe Gabel, Ehud Rivlin, Ilan Shimshoni
Sparse and redundant representations, where signals are modeled as a combination of a few atoms from an overcomplete dictionary, is increasingly used in many image processing applications, such as denoising, super resolution, and classification. One common problem is learning a "good" dictionary for different tasks. In the classification task the aim is to learn a dictionary that also takes training labels into account, and indeed there exist several approaches to this problem. One well-known technique is D-KSVD, which jointly learns a dictionary and a linear classifier using the K-SVD algorithm...
March 23, 2016: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28113265/top-down-visual-saliency-via-joint-crf-and-dictionary-learning
#16
Jimei Yang, Ming-Hsuan Yang
Top-down visual saliency is an important module of visual attention. In this work, we propose a novel top-down saliency model that jointly learns a Conditional Random Field (CRF) and a visual dictionary. The proposed model incorporates a layered structure from top to bottom: CRF, sparse coding and image patches. With sparse coding as an intermediate layer, CRF is learned in a feature-adaptive manner; meanwhile with CRF as the output layer, the dictionary is learned under structured supervision. For efficient and effective joint learning, we develop a max-margin approach via a stochastic gradient descent algorithm...
March 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28113171/cross-view-action-recognition-via-transferable-dictionary-learning
#17
Jingjing Zheng, Zhuolin Jiang, Rama Chellappa
Discriminative appearance features are effective for recognizing actions in a fixed view, but may not generalize well to a new view. In this paper, we present two effective approaches to learn dictionaries for robust action recognition across views. In the first approach, we learn a set of view-specific dictionaries where each dictionary corresponds to one camera view. These dictionaries are learned simultaneously from the sets of correspondence videos taken at different views with the aim of encouraging each video in the set to have the same sparse representation...
June 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28110736/alzheimer-s-disease-detection-via-automatic-3d-caudate-nucleus-segmentation-using-coupled-dictionary-learning-with-level-set-formulation
#18
Saif Dawood Salman Al-Shaikhli, Michael Ying Yang, Bodo Rosenhahn
BACKGROUND AND OBJECTIVE: This paper presents a novel method for Alzheimer's disease classification via an automatic 3D caudate nucleus segmentation. METHODS: The proposed method consists of segmentation and classification steps. In the segmentation step, we propose a novel level set cost function. The proposed cost function is constrained by a sparse representation of local image features using a dictionary learning method. We present coupled dictionaries: a feature dictionary of a grayscale brain image and a label dictionary of a caudate nucleus label image...
December 2016: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28103283/orthogonal-procrustes-analysis-for-dictionary-learning-in-sparse-linear-representation
#19
Giuliano Grossi, Raffaella Lanzarotti, Jianyi Lin
In the sparse representation model, the design of overcomplete dictionaries plays a key role for the effectiveness and applicability in different domains. Recent research has produced several dictionary learning approaches, being proven that dictionaries learnt by data examples significantly outperform structured ones, e.g. wavelet transforms. In this context, learning consists in adapting the dictionary atoms to a set of training signals in order to promote a sparse representation that minimizes the reconstruction error...
2017: PloS One
https://www.readbyqxmd.com/read/28092535/super-resolution-person-re-identification-with-semi-coupled-low-rank-discriminant-dictionary-learning
#20
Xiao-Yuan Jing, Xiaoke Zhu, Fei Wu, Ruimin Hu, Xinge You, Yunhong Wang, Hui Feng, Jing-Yu Yang
Person re-identification has been widely studied due to its importance in surveillance and forensics applications. In practice, gallery images are high resolution (HR), while probe images are usually low resolution (LR) in the identification scenarios with large variation of illumination, weather, or quality of cameras. Person re-identification in this kind of scenarios, which we call super-resolution (SR) person re-identification, has not been well studied. In this paper, we propose a semi-coupled low-rank discriminant dictionary learning (SLD(2)L) approach for SR person re-identification task...
March 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
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