One key in image quality assessment (IQA) is the design of image representations that can capture the changes of image structures caused by distortions. Recent studies show that sparsecoding has emerged as a promisin...
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One key in image quality assessment (IQA) is the design of image representations that can capture the changes of image structures caused by distortions. Recent studies show that sparsecoding has emerged as a promising approach to analyzing image structures for IQA. However, existing sparse-coding-based IQA approaches use linear coding models, which ignore the nonlinearities of manifolds of image patches and thus cannot analyze complex image structures well. To overcome such a weakness, in this paper, we introduce nonlinear sparsecoding to IQA. A kernel dictionary construction scheme is proposed, which combines analytic dictionaries and learnable dictionaries to guarantee both the stability and effectiveness of kernel sparse coding in the context of IQA. Built upon the kernel dictionary construction, an effective full-reference IQA metric is developed. Benefiting from the considerations on nonlinearities during sparsecoding, the proposed IQA metric not only characterizes image distortions better, but also achieves improvement on the consistency with subjective perception, when compared to the metrics built upon linear sparsecoding. Such benefits are demonstrated with the experimental results on eight benchmark datasets in terms of common criteria.
Paroxysmal Atrial Fibrillation (PAF) is a kind of accidental arrhythmia, and its high missed detection rate leads to the increase of heart-related diseases. An automatic detection method is proposed based on kernel sp...
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Paroxysmal Atrial Fibrillation (PAF) is a kind of accidental arrhythmia, and its high missed detection rate leads to the increase of heart-related diseases. An automatic detection method is proposed based on kernel sparse coding, which can identify PAF attacks based only on short RR interval data. A special geometric structure is presented to analyze the high-dimensional characteristics of the data, and the covariance matrix is calculated as a feature descriptor to find the Riemannian manifold structure contained in the data;Based on the Log-Euclidean framework, a manifold method is used to map the manifold space to a high-dimensional renewable kernel Hilbert space to obtain a more accurate sparse representation to identify quickly PAF. After verification by the Massa-chusetts Institute of Technology-Beth Israel Hospital atrial fibrillation database, the sensitivity is 98.71%, the specificity is 98.43%, and the total accuracy rate is 98.57%. Therefore, this study has a substantial improvement in the detection of transient PAF and shows good potential for clinical monitoring and treatment.
In this article, we aim to improve the performance of visual tracking by combing different features of multiple modalities. The core idea is to use covariance matrices as feature descriptors and then use sparsecoding...
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In this article, we aim to improve the performance of visual tracking by combing different features of multiple modalities. The core idea is to use covariance matrices as feature descriptors and then use sparsecoding to encode different features. The notion of sparsity has been successfully used in visual tracking. In this context, sparsity is used along appearance models often obtained from intensity/color information. In this work, we step outside this trend and propose to model the target appearance by local covariance descriptors (CovDs) in a pyramid structure. The proposed pyramid structure not only enables us to encode local and spatial information of the target appearance but also inherits useful properties of CovDs such as invariance to affme transforms. Since CovDs lie on a Riemannian manifold, we further propose to perform tracking through sparsecoding by embedding the Riemannian manifold into an infinite-dimensional Hilbert space. Embedding the manifold into a Hilbert space allows us to perform sparsecoding efficiently using the kernel trick. Our empirical study shows that the proposed tracking framework outperforms the existing state-ofthe-art methods in challenging scenarios.
In recent years, kernel-based sparsecoding (K-SRC) has received a special attention due to its efficient representation of nonlinear data structures in the feature space. Nevertheless, the existing K-SRC methods suff...
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ISBN:
(纸本)9781538691595
In recent years, kernel-based sparsecoding (K-SRC) has received a special attention due to its efficient representation of nonlinear data structures in the feature space. Nevertheless, the existing K-SRC methods suffer from the lack of consistency between their training and test optimization frameworks. In this work, we propose a novel confident K-SRC and dictionary learning algorithm (CKSC) which focuses on the discriminative reconstruction of the data based on its representation in the kernel space. CKSC focuses on reconstructing each data sample via weighted contributions which are confident in its corresponding class of data. We employ novel discriminative terms to apply this scheme to both training and test frameworks in our algorithm. This increases the consistency of these optimization frameworks and improves the discriminative performance in the recall phase. In addition, CKSC directly employs the supervised information in its dictionary learning framework to enhance the discriminative structure of the dictionary. For empirical evaluations, we implement our CKSC algorithm on multivariate time-series benchmarks such as DynTex++ and UTKinect. Our claims regarding the superior performance of the proposed algorithm are justified throughout comparing its classification results to the state-of-the-art K-SRC algorithms.
Automatic target recognition in infrared imagery is a challenging problem. In this paper, a kernel sparse coding method for infrared target recognition using covariance descriptor is proposed. First, covariance descri...
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Automatic target recognition in infrared imagery is a challenging problem. In this paper, a kernel sparse coding method for infrared target recognition using covariance descriptor is proposed. First, covariance descriptor combining gray intensity and gradient information of the infrared target is extracted as a feature representation. Then, due to the reason that covariance descriptor lies in non-Euclidean manifold, kernel sparse coding theory is used to solve this problem. We verify the efficacy of the proposed algorithm in terms of the confusion matrices on the real images consisting of seven categories of infrared vehicle targets. (C) 2016 Elsevier B.V. All rights reserved.
作者：
Liu, HuapingGuo, DiSun, FuchunTsinghua Univ
Tsinghua Natl Lab Informat Sci & Technol State Key Lab Intelligent Technol & Syst Dept Comp Sci & Technol Beijing 100084 Peoples R China
Dexterous robots have emerged in the last decade in response to the need for fine-motor-control assistance in applications as diverse as surgery, undersea welding, and mechanical manipulation in space. Crucial to the ...
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Dexterous robots have emerged in the last decade in response to the need for fine-motor-control assistance in applications as diverse as surgery, undersea welding, and mechanical manipulation in space. Crucial to the fine operation and contact environmental perception are tactile sensors that are fixed on the robotic fingertips. These can be used to distinguish material texture, roughness, spatial features, compliance, and friction. In this paper, we regard the investigated tactile data as time sequences, of which dissimilarity can be evaluated by the popular dynamic time warping method. A kernel sparse coding method is therefore developed to address the tactile data representation and classification problem. However, the naive use of sparsecoding neglects the intrinsic relation between individual fingers, which simultaneously contact the object. To tackle this problem, we develop a joint kernel sparse coding model to solve the multifinger tactile sequence classification problem. In this model, the intrinsic relations between fingers are explicitly taken into account using the joint sparsecoding, which encourages all of the coding vectors to share the same sparsity support pattern. The experimental results show that the joint sparsecoding achieves better performance than conventional sparsecoding.
We are interested in a decomposition of motion data into a sparse linear combination of base functions which enable efficient data processing. We combine two prominent frameworks: dynamic time warping (DTW), which off...
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ISBN:
(纸本)9783319447810;9783319447803
We are interested in a decomposition of motion data into a sparse linear combination of base functions which enable efficient data processing. We combine two prominent frameworks: dynamic time warping (DTW), which offers particularly successful pairwise motion data comparison, and sparsecoding (SC), which enables an automatic decomposition of vectorial data into a sparse linear combination of base vectors. We enhance SC via efficient kernelization which extends its application domain to general similarity data such as offered by DTW, and its restriction to non-negative linear representations of signals and base vectors in order to guarantee a meaningful dictionary. We also implemented the proposed method in a classification framework and evaluated its performance on various motion capture benchmark data sets.
When there are a few labeled images, the classifier trained performs poorly even we use sparsecoding technique to process image features. So we utilize other data from related domains as source data to help classific...
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When there are a few labeled images, the classifier trained performs poorly even we use sparsecoding technique to process image features. So we utilize other data from related domains as source data to help classification tasks. In this paper, we propose a Supervised Transfer kernel sparse coding (STKSC) algorithm to construct discriminative sparse representations for cross domain image classification tasks. Specifically, we map source and target data into a high dimensional feature space by using kernel trick, hence capturing the nonlinear image features. In order to make the sparse representations robust to the domain mismatch, we incorporate the Maximum Mean Discrepancy (MMD) criterion into the objective function of kernel sparse coding. We also use label information to learn more discriminative sparse representations. Furthermore, we provide a unified framework to learn the dictionary and the discriminative sparse representations, which can be further used for classification. The experiment results validate that our method outperforms many state-of-art methods. (C) 2015 Elsevier B.V. All rights reserved.
Tactile sensors in the robotic fingertips are used to capture multiple object properties such as texture, roughness, spatial features, compliance or friction and therefore becomes a very important sense modality for i...
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ISBN:
(纸本)9781479919598
Tactile sensors in the robotic fingertips are used to capture multiple object properties such as texture, roughness, spatial features, compliance or friction and therefore becomes a very important sense modality for intelligent robot. However, existing work neglects the intrinsic relation between different fingers which simultaneously contact the object. In this paper, a joint kernel sparse coding model is developed to tackle the multi-finger tactile sequence classification problem. In this model, the intrinsic relations between fingers are explicitly considered using the joint sparsecoding which encourages different modal coding to share the same support. The experimental results show that the joint sparsecoding achieves better performance than conventional sparsecoding.
Tactile sensors in the robotic fingertips are used to capture multiple object properties such as texture, roughness, spatial features, compliance or friction and therefore becomes a very important sense modality for i...
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ISBN:
(纸本)9781479919611
Tactile sensors in the robotic fingertips are used to capture multiple object properties such as texture, roughness, spatial features, compliance or friction and therefore becomes a very important sense modality for intelligent robot. However, existing work neglects the intrinsic relation between different fingers which simultaneously contact the object. In this paper, a joint kernel sparse coding model is developed to tackle the multi-finger tactile sequence classification problem. In this model, the intrinsic relations between fingers are explicitly considered using the joint sparsecoding which encourages different modal coding to share the same support. The experimental results show that the joint sparsecoding achieves better performance than conventional sparsecoding.
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