Many techniques have been recently developed for classification of hyperspectral images (HSI) including support vector machines (SVMs), neural networks and graph-based methods. To achieve good performances for the cla...
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ISBN:
(纸本)9780819490773
Many techniques have been recently developed for classification of hyperspectral images (HSI) including support vector machines (SVMs), neural networks and graph-based methods. To achieve good performances for the classification, a good feature representation of the HSI is essential. A great deal of feature extraction algorithms have been developed such as principal component analysis (PCA) and independent component analysis (ICA). sparse coding has recently shown state-of-the-art performances in many applications including image classification. In this paper, we present a feature extraction method for HSI data motivated by a recently developed sparse coding based image representation technique. sparse coding consists of a dictionary learning step and an encoding step. In the learning step, we compared two different methods, L1-penalized sparse coding and random selection for the dictionary learning. In the encoding step, we utilized a soft threshold activation function to obtain feature representations for HSI. We applied the proposed algorithm to a HSI dataset collected at the Kennedy Space Center (KSC) and compared our results with those obtained by a recently proposed method, supervised locally linear embedding weighted k-nearest-neighbor (SLLE-WkNN) classifier. We have achieved better performances on this dataset in terms of the overall accuracy with a random dictionary. We conclude that this simple feature extraction framework might lead to more efficient HSI classification systems.
As a promising technique, sparse coding has been widely used for the analysis, representation, compression, denoising and separation of speech. This technique needs a good dictionary which contains atoms to represent ...
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As a promising technique, sparse coding has been widely used for the analysis, representation, compression, denoising and separation of speech. This technique needs a good dictionary which contains atoms to represent speech signals. Although many methods have been proposed to learn such a dictionary, there are still two problems. First, unimportant atoms bring a heavy computational load to sparse decomposition and reconstruction, which prevents sparse coding from real-time application. Second, in speech denoising and separation, harmful atoms have no or ignorable contributions to reducing the sparsity degree but increase the source confusion, resulting in severe distortions. To solve these two problems, we first analyze the inherent assumptions of sparse coding and show that distortion can be caused if the assumptions do not hold true. Next, we propose two methods to optimize a given dictionary by removing unimportant atoms and harmful atoms, respectively. Experiments show that the proposed methods can further improve the performance of dictionaries. (C) 2015 Elsevier B.V. All rights reserved.
We consider the problem of dictionary learning and sparse coding, where the task is to find a concise set of basis vectors that accurately represent the observation data with only small numbers of active bases. Typica...
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We consider the problem of dictionary learning and sparse coding, where the task is to find a concise set of basis vectors that accurately represent the observation data with only small numbers of active bases. Typically formulated as an L1-regularized least-squares problem, the problem incurs computational difficulty originating from the nondifferentiable objective. Recent approaches to sparse coding thus have mainly focused on acceleration of the learning algorithm. In this paper, we propose an even more efficient and scalable sparse coding algorithm based on the first-order smooth optimization technique. The algorithm finds the theoretically guaranteed optimal sparse codes of the epsilon-approximate problem in a series of optimization subproblems, where each subproblem admits analytic solution, hence very fast and scalable with large-scale data. We further extend it to nonlinear sparse coding using kernel trick by showing that the representer theorem holds for the kernel sparse coding problem. This allows us to apply dual optimization, which essentially results in the same linear sparse coding problem in dual variables, highly beneficial compared with the existing methods that suffer from local minima and restricted forms of kernel function. The efficiency of our algorithms is demonstrated for natural stimuli data sets and several image classification problems.
sparse coding has been popularly used as an effective data representation method in various applications, such as computer vision, medical imaging and bioinformatics. However, the conventional sparse coding algorithms...
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sparse coding has been popularly used as an effective data representation method in various applications, such as computer vision, medical imaging and bioinformatics. However, the conventional sparse coding algorithms and their manifold-regularized variants (graph sparse coding and Laplacian sparse coding), learn codebooks and codes in an unsupervised manner and neglect class information that is available in the training set. To address this problem, we propose a novel discriminative sparse coding method based on multi-manifolds, that learns discriminative class-conditioned codebooks and sparse codes from both data feature spaces and class labels. First, the entire training set is partitioned into multiple manifolds according to the class labels. Then, we formulate the sparse coding as a manifold-manifold matching problem and learn class-conditioned codebooks and codes to maximize the manifold margins of different classes. Lastly, we present a data sample-manifold matching-based strategy to classify the unlabeled data samples. Experimental results on somatic mutations identification and breast tumor classification based on ultrasonic images demonstrate the efficacy of the proposed data representation and classification approach. (C) 2013 The Authors. Published by Elsevier B.V. All rights reserved.
Biomedical time series clustering that groups a set of unlabelled temporal signals according to their underlying similarity is very useful for biomedical records management and analysis such as biosignals archiving an...
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Biomedical time series clustering that groups a set of unlabelled temporal signals according to their underlying similarity is very useful for biomedical records management and analysis such as biosignals archiving and diagnosis. In this paper, a new framework for clustering of long-term biomedical time series such as electrocardiography (ECG) and electroencephalography (EEG) signals is proposed. Specifically, local segments extracted from the time series are projected as a combination of a small number of basis elements in a trained dictionary by non-negative sparse coding. A Bag-of-Words (BoW) representation is then constructed by summing up all the sparse coefficients of local segments in a time series. Based on the BoW representation, a probabilistic topic model that was originally developed for text document analysis is extended to discover the underlying similarity of a collection of time series. The underlying similarity of biomedical time series is well captured attributing to the statistic nature of the probabilistic topic model. Experiments on three datasets constructed from publicly available EEG and ECG signals demonstrates that the proposed approach achieves better accuracy than existing state-of-the-art methods, and is insensitive to model parameters such as length of local segments and dictionary size. (c) 2013 Elsevier Ireland Ltd. All rights reserved.
In this paper, considering that the trained dictionary pairs by sparse coding based super resolution (SR) methods have difficulty capturing the complicated nonlinear relationships between the low-resolution (LR) and h...
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In this paper, considering that the trained dictionary pairs by sparse coding based super resolution (SR) methods have difficulty capturing the complicated nonlinear relationships between the low-resolution (LR) and high-resolution (HR) feature spaces, we propose a new single image SR method by combining sparse coding with the improved structured output regression machine (SORM). In the proposed method, the dictionary pairs are firstly learned by joint sparse coding to characterize the structural domain of each feature space and add more consistency between the sparse codes of two feature spaces. Then, since the classical SORM does not give sufficient weight to the independence of different output components, we improve the SORM by considering the correlation and independence between different output components to establish a set of mapping functions for tying the sparse code of two feature spaces. With this, the more precise mapping relationships between two feature spaces are obtained by the trained dictionary pairs and mapping functions. Moreover, we propose a new global and nonlocal optimization for further enhancing the quality of the restored HR images. Extensive experiments validate that the proposed method can achieve convincing improvement over other state-of-the-art methods in terms of the reconstruction quality and computational cost. (C) 2017 Elsevier Inc. All rights reserved.
Rainfall weather always degrades the quality of the images severely in the outdoor surveillance system. To improve the quality of images, different rain removal algorithms have been proposed recently. As the decomposi...
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Rainfall weather always degrades the quality of the images severely in the outdoor surveillance system. To improve the quality of images, different rain removal algorithms have been proposed recently. As the decomposition based methods do not need to impose any restrictions on the types of rain, they have a wider application prospect. However, they still have the problems of rain residue in the low-frequency components and information loss in high-frequency components. To solve these problems, we propose an image rain streak removal algorithm based on the depth of field (DoF) and sparse coding. Firstly, we decompose the image by using the combination of bilateral filtering and short-time Fourier transform, so that the contour in the low-frequency part of the image can be better preserved. Then the DoF saliency map of the image is used both to reduce the rain residue in the low-frequency components and to avoid mis-matching the background and the rain streaks with the same gradient in the high-frequency components. We use DoF saliency map as the weights for the weighted sum of the original image and the initial low-frequency image to obtain the corrected low-frequency images. The DoF saliency map is also used to twice weaken the rain streaks in the high-frequency image to generate the corrected high-frequency images. The algorithm includes four steps: image decomposition, dictionary learning, atomic clustering based on Principal Component Analysis and Support Vector Machine, image revising based on DoF saliency map. The experimental results demonstrate that our proposed algorithm performs better both in rain removal and high-frequency information preserving than current methods. (C) 2018 Elsevier B.V. All rights reserved.
This paper presents a novel noise-robust graph-based semi-supervised learning algorithm to deal with the challenging problem of semi-supervised learning with noisy initial labels. Inspired by the successful use of spa...
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This paper presents a novel noise-robust graph-based semi-supervised learning algorithm to deal with the challenging problem of semi-supervised learning with noisy initial labels. Inspired by the successful use of sparse coding for noise reduction, we choose to give new L-1-norm formulation of Laplacian regularization for graph-based semi-supervised learning. Since our L-1-norm Laplacian regularization is explicitly defined over the eigenvectors of the normalized Laplacian matrix, we formulate graph-based semi-supervised learning as an L-1-norm linear reconstruction problem which can be efficiently solved by sparse coding. Furthermore, by working with only a small subset of eigenvectors, we develop a fast sparse coding algorithm for our L-1-norm semi-supervised learning. Finally, we evaluate the proposed algorithm in noise-robust image classification. The experimental results on several benchmark datasets demonstrate the promising performance of the proposed algorithm. (C) 2014 Elsevier Ltd. All rights reserved.
Depression is a severe psychiatric disorder preventing a person from functioning normally in both work and daily lives. Currently, diagnosis of depression requires extensive participation from clinical experts. It has...
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Depression is a severe psychiatric disorder preventing a person from functioning normally in both work and daily lives. Currently, diagnosis of depression requires extensive participation from clinical experts. It has drawn much attention to develop an automatic system for efficient and reliable diagnosis of depression. Under the influence of depression, visual-based behavior disorder is readily observable. This paper presents a novel method of exploring facial region visual-based nonverbal behavior analysis for automatic depression diagnosis. Dynamic feature descriptors are extracted from facial region subvolumes, and sparse coding is employed to implicitly organize the extracted feature descriptors for depression diagnosis. Discriminative mapping and decision fusion are applied to further improve the accuracy of visual-based diagnosis. The integrated approach has been tested on the AVEC2013 depression database and the best visual-based mean absolute error/root mean square error results have been achieved.
Human activity analysis in videos has increasingly attracted attention in computer vision research with the massive number of videos now accessible online. Although many recognition algorithms have been reported recen...
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Human activity analysis in videos has increasingly attracted attention in computer vision research with the massive number of videos now accessible online. Although many recognition algorithms have been reported recently, activity representation is challenging. Recently, manifold regularized sparse coding has obtained promising performance in action recognition, because it simultaneously learns the sparse representation and preserves the manifold structure. In this paper, we propose a generalized version of Laplacian regularized sparse coding for human activity recognition called p-Laplacian regularized sparse coding (pLSC). The proposed method exploits p-Laplacian regularization to preserve the local geometry. The p-Laplacian is a nonlinear generalization of standard graph Laplacian and has tighter isoperimetric inequality. As a result, pLSC provides superior theoretical evidence than standard Laplacian regularized sparse coding with a proper p. We also provide a fast iterative shrinkage-thresholding algorithm for the optimization of pLSC. Finally, we input the sparse codes learned by the pLSC algorithm into support vector machines and conduct extensive experiments on the unstructured social activity attribute dataset and human motion database (HMDB51) for human activity recognition. The experimental results demonstrate that the proposed pLSC algorithm outperforms the manifold regularized sparse coding algorithms including the standard Laplacian regularized sparse coding algorithm with a proper p.
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