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Dictionary learning

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https://www.readbyqxmd.com/read/27925590/modality-invariant-image-classification-based-on-modality-uniqueness-and-dictionary-learning
#1
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/27893388/sparse-representation-based-multiple-frame-video-super-resolution
#2
Qiqin Dai, Seunghwan Yoo, Armin Kappeler, Aggelos K Katsaggelos
In this paper, we propose two multiple-frame superresolution (SR) algorithms based on dictionary learning and motion estimation. First, we adopt the use of video bilevel dictionary learning which has been used for single-frame SR. It is extended to multiple frames by using motion estimation with subpixel accuracy. We propose a batch and a temporally recursive multi-frame SR algorithm, which improve over single frame SR. Finally, we propose a novel dictionary learning algorithm utilizing consecutive video frames, rather than still images or individual video frames, which further improves the performance of the video SR algorithms...
November 22, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27868105/feasibility-of-automated-3-dimensional-magnetic-resonance-imaging-pancreas-segmentation
#3
Shuiping Gou, Percy Lee, Peng Hu, Jean-Claude Rwigema, Ke Sheng
PURPOSE: With the advent of MR guided radiotherapy, internal organ motion can be imaged simultaneously during treatment. In this study, we evaluate the feasibility of pancreas MRI segmentation using state-of-the-art segmentation methods. METHODS AND MATERIAL: T2 weighted HASTE and T1 weighted VIBE images were acquired on 3 patients and 2 healthy volunteers for a total of 12 imaging volumes. A novel dictionary learning (DL) method was used to segment the pancreas and compared to t mean-shift merging (MSM), distance regularized level set (DRLS), graph cuts (GC) and the segmentation results were compared to manual contours using Dice's index (DI), Hausdorff distance and shift of the-center-of-the-organ (SHIFT)...
July 2016: Advances in Radiation Oncology
https://www.readbyqxmd.com/read/27849551/information-clustering-using-manifold-based-optimization-of-the-bag-of-features-representation
#4
Nikolaos Passalis, Anastasios Tefas
In this paper, a manifold-based dictionary learning method for the bag-of-features (BoF) representation optimized toward information clustering is proposed. First, the spectral representation, which unwraps the manifolds of the data and provides better clustering solutions, is formed. Then, a new dictionary is learned in order to make the histogram space, i.e., the space where the BoF historgrams exist, as similar as possible to the spectral space. The ability of the proposed method to improve the clustering solutions is demonstrated using a wide range of datasets: two image datasets, the 15-scene dataset and the Corel image dataset, one video dataset, the KTH dataset, and one text dataset, the RT-2k dataset...
November 10, 2016: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/27837523/linguistic-evidence-for-the-failure-mindset-as-a-predictor-of-life-span-longevity
#5
Ian B Penzel, Michelle R Persich, Ryan L Boyd, Michael D Robinson
BACKGROUND: When people think that their efforts will fail to achieve positive outcomes, they sometimes give up their efforts after control, which can have negative health consequences. PURPOSE: Problematic orientations of this type, such as pessimism, helplessness, or fatalism, seem likely to be associated with a cognitive mindset marked by higher levels of accessibility for failure words or concepts. Thus, the purpose of the present research was to determine whether there are individual differences in the frequency with which people think about failure, which in turn are likely to impact health across large spans of time...
November 11, 2016: Annals of Behavioral Medicine: a Publication of the Society of Behavioral Medicine
https://www.readbyqxmd.com/read/27831875/bilevel-model-based-discriminative-dictionary-learning-for-recognition
#6
Pan Zhou, Chao Zhang, Zhouchen Lin
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 `0 or `1 norm to encode each datum independently, limiting the performance of the learnt dictionaries...
October 31, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27831874/content-adaptive-sketch-portrait-generation-by-decompositional-representation-learning
#7
Dongyu Zhang, Liang Lin, Tianshui Chen, Xian Wu, Wenwei Tan, Ebroul Izquierdo
Sketch portrait generation benefits a wide range of applications such as digital entertainment and law enforcement. Although plenty of efforts have been dedicated to this task, several issues still remain unsolved for generating vivid and detail-preserving personal sketch portraits. For example, quite a few artifacts may exist in synthesizing hairpins and glasses, and textural details may be lost in the regions of hair or mustache. Moreover, the generalization ability of current systems is somewhat limited since they usually require elaborately collecting a dictionary of examples or carefully tuning features/components...
January 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27831873/multi-modal-dictionary-learning-for-image-separation-with-application-in-art-investigation
#8
Nikos Deligiannis, Joao F C Mota, Bruno Cornelis, Miguel R D Rodrigues, Ingrid Daubechies
In support of art investigation, we propose a new source separation method that unmixes a single X-ray scan acquired from double-sided paintings. In this problem, the X-ray signals to be separated have similar morphological characteristics, which brings previous source separation methods to their limits. Our solution is to use photographs taken from the front-and back-side of the panel to drive the separation process. The crux of our approach relies on the coupling of the two imaging modalities (photographs and X-rays) using a novel coupled dictionary learning framework able to capture both common and disparate features across the modalities using parsimonious representations; the common component captures features shared by the multi-modal images, whereas the innovation component captures modality-specific information...
October 31, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27831872/sparsity-based-image-error-concealment-via-adaptive-dual-dictionary-learning-and-regularization
#9
Xianming Liu, Deming Zhai, Jiantao Zhou, Shiqi Wang, Debin Zhao, Huijun Gao
In this paper, we propose a novel sparsity-based image error concealment (EC) algorithm through Adaptive Dual dictionary Learning and Regularization (ADLR). We define two feature spaces: the observed space and the latent space, corresponding to the available regions and the missing regions of image under test, respectively. We learn adaptive and complete dictionaries individually for each space, where the training data are collected via an adaptive template matching mechanism. Based on the piecewise stationarity of natural images, a local correlation model is learned to bridge the sparse representations of the aforementioned dual spaces, allowing us to transfer the knowledge of the available regions to the missing regions for EC purpose...
October 31, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27818565/multi-scale-learning-based-segmentation-of-glands-in-digital-colonrectal-pathology-images
#10
Yi Gao, William Liu, Shipra Arjun, Liangjia Zhu, Vadim Ratner, Tahsin Kurc, Joel Saltz, Allen Tannenbaum
Digital histopathological images provide detailed spatial information of the tissue at micrometer resolution. Among the available contents in the pathology images, meso-scale information, such as the gland morphology, texture, and distribution, are useful diagnostic features. In this work, focusing on the colon-rectal cancer tissue samples, we propose a multi-scale learning based segmentation scheme for the glands in the colon-rectal digital pathology slides. The algorithm learns the gland and non-gland textures from a set of training images in various scales through a sparse dictionary representation...
February 2016: Proceedings of SPIE
https://www.readbyqxmd.com/read/27816858/automatic-apical-view-classification-of-echocardiograms-using-a-discriminative-learning-dictionary
#11
Hanan Khamis, Grigoriy Zurakhov, Vered Azar, Adi Raz, Zvi Friedman, Dan Adam
As part of striving towards fully automatic cardiac functional assessment of echocardiograms, automatic classification of their standard views is essential as a pre-processing stage. The similarity among three of the routinely acquired longitudinal scans: apical two-chamber (A2C), apical four-chamber (A4C) and apical long-axis (ALX), and the noise commonly inherent to these scans - make the classification a challenge. Here we introduce a multi-stage classification algorithm that employs spatio-temporal feature extraction (Cuboid Detector) and supervised dictionary learning (LC-KSVD) approaches to uniquely enhance the automatic recognition and classification accuracy of echocardiograms...
October 24, 2016: Medical Image Analysis
https://www.readbyqxmd.com/read/27806598/stratification-of-pseudoprogression-and-true-progression-of-glioblastoma-multiform-based-on-longitudinal-diffusion-tensor-imaging-without-segmentation
#12
Xiaohua Qian, Hua Tan, Jian Zhang, Weilin Zhao, Michael D Chan, Xiaobo Zhou
PURPOSE: Pseudoprogression (PsP) can mimic true tumor progression (TTP) on magnetic resonance imaging in patients with glioblastoma multiform (GBM). The phenotypical similarity between PsP and TTP makes it a challenging task for physicians to distinguish these entities. So far, no approved biomarkers or computer-aided diagnosis systems have been used clinically for this purpose. METHODS: To address this challenge, the authors developed an objective classification system for PsP and TTP based on longitudinal diffusion tensor imaging...
November 2016: Medical Physics
https://www.readbyqxmd.com/read/27782724/computer-aided-diagnosis-of-prostate-cancer-a-texton-based-approach
#13
Andrik Rampun, Bernie Tiddeman, Reyer Zwiggelaar, Paul Malcolm
PURPOSE: In this paper the authors propose a texton based prostate computer aided diagnosis approach which bypasses the typical feature extraction process such as filtering and convolution which can be computationally expensive. The study focuses the peripheral zone because 75% of prostate cancers start within this region and the majority of prostate cancers arising within this region are more aggressive than those arising in the transitional zone. METHODS: For the model development, square patches were extracted at random locations from malignant and benign regions...
October 2016: Medical Physics
https://www.readbyqxmd.com/read/27777215/-when-bad-is-good-identifying-personal-communication-and-sentiment-in-drug-related-tweets
#14
Raminta Daniulaityte, Lu Chen, Francois R Lamy, Robert G Carlson, Krishnaprasad Thirunarayan, Amit Sheth
BACKGROUND: To harness the full potential of social media for epidemiological surveillance of drug abuse trends, the field needs a greater level of automation in processing and analyzing social media content. OBJECTIVES: The objective of the study is to describe the development of supervised machine-learning techniques for the eDrugTrends platform to automatically classify tweets by type/source of communication (personal, official/media, retail) and sentiment (positive, negative, neutral) expressed in cannabis- and synthetic cannabinoid-related tweets...
October 24, 2016: JMIR Public Health and Surveillance
https://www.readbyqxmd.com/read/27775546/extreme-kernel-sparse-learning-for-tactile-object-recognition
#15
Huaping Liu, Jie Qin, Fuchun Sun, Di Guo
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...
October 19, 2016: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/27775515/adaptive-greedy-dictionary-selection-for-web-media-summarization
#16
Yang Cong, Ji Liu, Gan Sun, Quanzeng You, Yuncheng Li, Jiebo Luo
Initializing an effective dictionary is an indispensable step for sparse representation. In this paper, we focus on the dictionary selection problem with the objective to select a compact subset of basis from original training data instead of learning a new dictionary matrix as dictionary learning models do. We first design a new dictionary selection model via `2;0 norm. For model optimization, we propose two methods: one is the standard forward-backward greedy algorithm, which is not suitable for large-scale problems; the other is based on the gradient cues at each forward iteration and speeds up the process dramatically...
October 19, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27766937/leveraging-graph-topology-and-semantic-context-for-pharmacovigilance-through-twitter-streams
#17
Ryan Eshleman, Rahul Singh
BACKGROUND: Adverse drug events (ADEs) constitute one of the leading causes of post-therapeutic death and their identification constitutes an important challenge of modern precision medicine. Unfortunately, the onset and effects of ADEs are often underreported complicating timely intervention. At over 500 million posts per day, Twitter is a commonly used social media platform. The ubiquity of day-to-day personal information exchange on Twitter makes it a promising target for data mining for ADE identification and intervention...
October 6, 2016: BMC Bioinformatics
https://www.readbyqxmd.com/read/27764591/improving-the-incoherence-of-a-learned-dictionary-via-rank-shrinkage
#18
Shashanka Ubaru, Abd-Krim Seghouane, Yousef Saad
This letter considers the problem of dictionary learning for sparse signal representation whose atoms have low mutual coherence. To learn such dictionaries, at each step, we first updated the dictionary using the method of optimal directions (MOD) and then applied a dictionary rank shrinkage step to decrease its mutual coherence. In the rank shrinkage step, we first compute a rank 1 decomposition of the column-normalized least squares estimate of the dictionary obtained from the MOD step. We then shrink the rank of this learned dictionary by transforming the problem of reducing the rank to a nonnegative garrotte estimation problem and solving it using a path-wise coordinate descent approach...
October 20, 2016: Neural Computation
https://www.readbyqxmd.com/read/27763511/hierarchical-sparse-learning-with-spectral-spatial-information-for-hyperspectral-imagery-denoising
#19
Shuai Liu, Licheng Jiao, Shuyuan Yang
During the acquisition process hyperspectral images (HSI) are inevitably corrupted by various noises, which greatly influence their visual impression and subsequent applications. In this paper, a novel Bayesian approach integrating hierarchical sparse learning and spectral-spatial information is proposed for HSI denoising. Based on the structure correlations, spectral bands with similar and continuous features are segmented into the same band-subset. To exploit local similarity, each subset is then divided into overlapping cubic patches...
October 17, 2016: Sensors
https://www.readbyqxmd.com/read/27756284/introduction-of-digital-speech-recognition-in-a-specialised-outpatient-department-a-case-study
#20
Christoph Ahlgrim, Oliver Maenner, Manfred W Baumstark
BACKGROUND: Speech recognition software might increase productivity in clinical documentation. However, low user satisfaction with speech recognition software has been observed. In this case study, an approach for implementing a speech recognition software package at a university-based outpatient department is presented. METHODS: Methods to create a specific dictionary for the context "sports medicine" and a shared vocabulary learning function are demonstrated. The approach is evaluated for user satisfaction (using a questionnaire before and 10 weeks after software implementation) and its impact on the time until the final medical document was saved into the system...
October 18, 2016: BMC Medical Informatics and Decision Making
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