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Dictionary learning

Xiudong Wang, Yuantao Gu
This paper addresses image classification through learning a compact and discriminative dictionary efficiently. Given a structured dictionary with each atom (columns in the dictionary matrix) related to some label, we propose crosslabel suppression constraint to enlarge the difference among representations for different classes. Meanwhile, we introduce group regularization to enforce representations to preserve label properties of original samples, meaning the representations for the same class are encouraged to be similar...
May 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Zaidao Wen, Biao Hou, Licheng Jiao
Linear synthesis model based dictionary learning framework has achieved remarkable performances in image classification in the last decade. Behaved as a generative feature model, it however suffers from some intrinsic deficiencies. In this paper, we propose a novel parametric nonlinear analysis cosparse model (NACM) with which a unique feature vector will be much more efficiently extracted. Additionally, we derive a deep insight to demonstrate that NACM is capable of simultaneously learning the task adapted feature transformation and regularization to encode our preferences, domain prior knowledge and task oriented supervised information into the features...
May 3, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Haijun Wang, Xinbo Gao, Kaibing Zhang, Jie Li
Gaussian process regression (GPR) is an effective statistical learning method for modeling non-linear mapping from an observed space to an expected latent space. When applying it to example learning-based super-resolution (SR), two outstanding issues remain. One is its high computational complexity restricts SR application when a large dataset is available for learning task. The other is that the commonly used Gaussian likelihood in GPR is incompatible with the true observation model for SR reconstruction. To alleviate the above two issues, we propose a GPR-based SR method by using dictionary-based sampling and student-t likelihood, called DSGPR...
May 3, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Christian Weiss, Abdelhak M Zoubir
We propose a compressed sampling and dictionary learning framework for fiber-optic sensing using wavelength-tunable lasers. A redundant dictionary is generated from a model for the reflected sensor signal. Imperfect prior knowledge is considered in terms of uncertain local and global parameters. To estimate a sparse representation and the dictionary parameters, we present an alternating minimization algorithm that is equipped with a preprocessing routine to handle dictionary coherence. The support of the obtained sparse signal indicates the reflection delays, which can be used to measure impairments along the sensing fiber...
May 1, 2017: Journal of the Optical Society of America. A, Optics, Image Science, and Vision
Abd-Krim Seghouane, Asif Iqbal
Sequential dictionary learning algorithms have been successfully applied to functional magnetic resonance imaging (fMRI) data analysis. fMRI datasets are however structured data matrices with notions of temporal smoothness in the column direction. This prior information which can be converted to a constraint of smoothness on the learned dictionary atoms has seldomly been included in classical dictionary learning algorithms when applied to fMRI data analysis. In this paper we tackle this problem by proposing two new sequential dictionary learning algorithms dedicated to fMRI data analysis by accounting for this prior information...
April 28, 2017: IEEE Transactions on Medical Imaging
Junliang Xing, Zhiheng Niu, Junshi Huang, Weiming Hu, Xi Zhou, Shuicheng Yan
Face alignment acts as an important task in computer vision. Regression-based methods currently dominate the approach to solving this problem, which generally employ a series of mapping functions from the face appearance to iteratively update the face shape hypothesis. One keypoint here is thus how to perform the regression procedure. In this work, we formulate this regression procedure as a sparse coding problem. We learn two relational dictionaries, one for the face appearance and the other one for the face shape, with coupled reconstruction coefficient to capture their underlying relationships...
April 25, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Vineeta Das, Niladri B Puhan
Computer-assisted automated exudate detection is crucial for large-scale screening of diabetic retinopathy (DR). The motivation of this work is robust and accurate detection of low contrast and isolated hard exudates using fundus imaging. Gabor filtering is first performed to enhance exudate visibility followed by Tsallis entropy thresholding. The obtained candidate exudate pixel map is useful for further removal of falsely detected candidates using sparse-based dictionary learning and classification. Two reconstructive dictionaries are learnt using the intensity, gradient, local energy, and transform domain features extracted from exudate and background patches of the training fundus images...
April 2017: Journal of Medical Imaging
Peihua Li, Hui Zeng, Qilong Wang, Simon Shiu, Lei Zhang
Local pooling (LP) in configuration (feature) space proposed by Boureau et al. explicitly restricts similar features to be aggregated, which can preserve as much discriminative information as possible. At the time it appeared, this method combined with sparse coding achieved competitive classification results with only a small dictionary. However, its performance lags far behind state-of-the-art results as only zero-order information is exploited. Inspired by the success of high-order statistical information in existing advanced feature coding or pooling methods, we make an attempt to address the limitation of LP...
April 19, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Wenrui Dai, Yangmei Shen, Hongkai Xiong, Xiaoqian Jiang, Junni Zou, David Taubman
Dictionary learning has emerged as a promising alternative to the conventional hybrid coding framework. However, the rigid structure of sequential training and prediction degrades its performance in scalable video coding. This paper proposes a progressive dictionary learning framework with hierarchical predictive structure for scalable video coding, especially in low bitrate region. For pyramidal layers, sparse representation based on spatio-temporal dictionary is adopted to improve the coding efficiency of enhancement layers (ELs) with a guarantee of reconstruction performance...
April 12, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Qiuping Jiang, Feng Shao, Weisi Lin, Gangyi Jiang
The goal of image retargeting is to adapt source images to target displays with different sizes and aspect ratios. Different retargeting operators create different retargeted images, and a key problem is to evaluate the performance of each retargeting operator. Subjective evaluation is most reliable, but it is cumbersome and labor-consuming, and more importantly, it is hard to be embedded into online optimization systems. This paper focuses on exploring the effectiveness of sparse representation for objective image retargeting quality assessment...
April 13, 2017: IEEE Transactions on Cybernetics
Wei Zhou, Chengdong Wu, Dali Chen, Zhenzhu Wang, Yugen Yi, Wenyou Du
Recently, microaneurysm (MA) detection has attracted a lot of attention in the medical image processing community. Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL). The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a unified framework for automatic detection of MAs...
2017: Computational and Mathematical Methods in Medicine
Ganggang Dong, Gangyao Kuang, Na Wang, Wei Wang
Automatic target recognition has been studied widely over the years, yet it is still an open problem. The main obstacle consists in extended operating conditions, e.g., depression angle change, configuration variation, articulation, occlusion. To deal with them, this paper proposes a new classification strategy. We develop a new representation model via the steerable wavelet frames. The proposed representation model is entirely viewed as an element on Grassmann manifolds. To achieve target classification, we embed Grassmann manifolds into an implicit Reproducing Kernel Hilbert Space (RKHS), where the kernel sparse learning can be applied...
April 7, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Xian Wei, Yuanxiang Li, Hao Shen, Fang Chen, Martin Kleinsteuber, Zhongfeng Wang
Video representation is an important and challenging task in the computer vision community. In this paper, we consider the problem of modeling and classifying video sequences of dynamic scenes which could be modeled in a dynamic textures (DT) framework. At first, we assume that image frames of a moving scene can be modeled as a Markov random process. We propose a sparse coding framework, named joint video dictionary learning (JVDL), to model a video adaptively. By treating the sparse coefficients of image frames over a learned dictionary as the underlying "states", we learn an efficient and robust linear transition matrix between two adjacent frames of sparse events in time series...
April 6, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Reid Trenton Powell, Adriana Olar, Shivali Narang, Ganesh Rao, Erik Sulman, Gregory N Fuller, Arvind Rao
BACKGROUND: Glioma, the most common primary brain neoplasm, describes a heterogeneous tumor of multiple histologic subtypes and cellular origins. At clinical presentation, gliomas are graded according to the World Health Organization guidelines (WHO), which reflect the malignant characteristics of the tumor based on histopathological and molecular features. Lower grade diffuse gliomas (LGGs) (WHO Grade II-III) have fewer malignant characteristics than high-grade gliomas (WHO Grade IV), and a better clinical prognosis, however, accurate discrimination of overall survival (OS) remains a challenge...
2017: Journal of Pathology Informatics
Michael Bianco, Peter Gerstoft
To provide constraints on the inversion of ocean sound speed profiles (SSPs), SSPs are often modeled using empirical orthogonal functions (EOFs). However, this regularization, which uses the leading order EOFs with a minimum-energy constraint on the coefficients, often yields low resolution SSP estimates. In this paper, it is shown that dictionary learning, a form of unsupervised machine learning, can improve SSP resolution by generating a dictionary of shape functions for sparse processing (e.g., compressive sensing) that optimally compress SSPs; both minimizing the reconstruction error and the number of coefficients...
March 2017: Journal of the Acoustical Society of America
Jessica J M Monaghan, Tobias Goehring, Xin Yang, Federico Bolner, Shangqiguo Wang, Matthew C M Wright, Stefan Bleeck
Machine-learning based approaches to speech enhancement have recently shown great promise for improving speech intelligibility for hearing-impaired listeners. Here, the performance of three machine-learning algorithms and one classical algorithm, Wiener filtering, was compared. Two algorithms based on neural networks were examined, one using a previously reported feature set and one using a feature set derived from an auditory model. The third machine-learning approach was a dictionary-based sparse-coding algorithm...
March 2017: Journal of the Acoustical Society of America
Juan Serra, Matteo Testa, Rafael Molina, Aggelos K Katsaggelos
Recent work in signal processing in general and image processing in particular deals with sparse representation related problems. Two such problems are of paramount importance: an overriding need for designing a well-suited overcomplete dictionary containing a redundant set of atoms - i.e., basis signals- and how to find a sparse representation of a given signal with respect to the chosen dictionary. Dictionary learning techniques, among which we find the popular K-Singular Value Decomposition (K-SVD) algorithm, tackle these problems by adapting a dictionary to a set of training data...
March 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Te Han, Dongxiang Jiang, Xiaochen Zhang, Yankui Sun
Rotating machinery is widely used in industrial applications. With the trend towards more precise and more critical operating conditions, mechanical failures may easily occur. Condition monitoring and fault diagnosis (CMFD) technology is an effective tool to enhance the reliability and security of rotating machinery. In this paper, an intelligent fault diagnosis method based on dictionary learning and singular value decomposition (SVD) is proposed. First, the dictionary learning scheme is capable of generating an adaptive dictionary whose atoms reveal the underlying structure of raw signals...
March 27, 2017: Sensors
Murad Megjhani, Pedro Correa de Sampaio, Julienne Leigh Carstens, Raghu Kalluri, Badrinath Roysam
Motivation: Current spectral unmixing methods for multiplex fluorescence microscopy have a limited ability to cope with high spectral overlap as they only analyze spectral information over individual pixels. Here, we present adaptive Morphologically Constrained Spectral Unmixing (MCSU) algorithms that overcome this limitation by exploiting morphological differences between sub-cellular structures, and their local spatial context. Results: The proposed method was effective at improving spectral unmixing performance by exploiting: (i) a set of dictionary-based models for object morphologies learned from the image data; and (ii) models of spatial context learned from the image data using a total variation algorithm...
March 2, 2017: Bioinformatics
Abd-Krim Seghouane, Asif Iqbal
Sequential dictionary learning via the K-SVD algorithm has been revealed as a successful alternative to conventional data driven methods such as independent component analysis (ICA) for functional magnetic resonance imaging (fMRI) data analysis. fMRI datasets are however structured data matrices with notions of spatio-temporal correlation and temporal smoothness. This prior information has not been included in the K-SVD algorithm when applied to fMRI data analysis. In this paper we propose three variants of the K-SVD algorithm dedicated to fMRI data analysis by accounting for this prior information...
March 22, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
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