The Experts below are selected from a list of 61065 Experts worldwide ranked by ideXlab platform
Mingting Sun - One of the best experts on this subject based on the ideXlab platform.
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video summarization based on nonnegative Linear Reconstruction
International Conference on Multimedia and Expo, 2014Co-Authors: Qiao Luan, Mingli Song, Chu Yee Liau, Zicheng Liu, Mingting SunAbstract:With the development of imaging techniques and the Internet, it is hard to effectively and efficiently manage, index and store large amounts of videos. Video summarization appears to this need by obtaining useful information in videos. Recently, the Reconstruction concept has been introduced into video summarization. However, most of these existing Reconstruction methods where a dictionary of key frames is selected to reconstruct the original video best might come up with redundant information. In this paper, we propose a novel framework named Video Summarization based on Nonnegative Linear Reconstruction (VSNLR) which allows only additive, not subtractive, Linear combinations. Our approach consists of two core steps: (1) we detect the shot boundary and choose a representative frame for every shot, then all the representative frames form the candidate set; (2) for every frame in the candidate set, VSNLR selects related frames to reconstruct the given frame by using the nonnegative Linear Reconstruction function. The key frames are selected by minimizing the sum of Reconstruction errors. Experiments on a dataset and comparison to the state-of-art method demonstrate our advantage.
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ICME - Video Summarization based on Nonnegative Linear Reconstruction
2014 IEEE International Conference on Multimedia and Expo (ICME), 2014Co-Authors: Qiao Luan, Mingli Song, Chu Yee Liau, Zicheng Liu, Mingting SunAbstract:With the development of imaging techniques and the Internet, it is hard to effectively and efficiently manage, index and store large amounts of videos. Video summarization appears to this need by obtaining useful information in videos. Recently, the Reconstruction concept has been introduced into video summarization. However, most of these existing Reconstruction methods where a dictionary of key frames is selected to reconstruct the original video best might come up with redundant information. In this paper, we propose a novel framework named Video Summarization based on Nonnegative Linear Reconstruction (VSNLR) which allows only additive, not subtractive, Linear combinations. Our approach consists of two core steps: (1) we detect the shot boundary and choose a representative frame for every shot, then all the representative frames form the candidate set; (2) for every frame in the candidate set, VSNLR selects related frames to reconstruct the given frame by using the nonnegative Linear Reconstruction function. The key frames are selected by minimizing the sum of Reconstruction errors. Experiments on a dataset and comparison to the state-of-art method demonstrate our advantage.
Nicholas J. Hills - One of the best experts on this subject based on the ideXlab platform.
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Large-Eddy Simulations of Wall Bounded Turbulent Flows Using Unstructured Linear Reconstruction Techniques
Journal of Turbomachinery, 2015Co-Authors: Dario Amirante, Nicholas J. HillsAbstract:Large-eddy simulations (LES) of wall bounded, low Mach number turbulent flows are conducted using an unstructured finite-volume solver of the compressible flow equations. The numerical method employs Linear Reconstructions of the primitive variables based on the least-squares approach of Barth. The standard Smagorinsky model is adopted as the subgrid term. The artificial viscosity inherent to the spatial discretization is maintained as low as possible reducing the dissipative contribution embedded in the approximate Riemann solver to the minimum necessary. Comparisons are also discussed with the results obtained using the implicit LES (ILES) procedure. Two canonical test-cases are described: a fully developed pipe flow at a bulk Reynolds number Reb = 44 × 103 based on the pipe diameter, and a confined rotor–stator flow at the rotational Reynolds number ReΩ = 4 × 105 based on the outer radius. In both cases, the mean flow and the turbulent statistics agree well with existing direct numerical simulations (DNS) or experimental data.
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LES OF WALL BOUNDED TURBULENT FLOWS USING UNSTRUCTURED Linear Reconstruction TECHNIQUES
Volume 2B: Turbomachinery, 2014Co-Authors: Dario Amirante, Nicholas J. HillsAbstract:Large-Eddy Simulations of wall bounded, low Mach number turbulent flows are conducted using an unstructured finite-volume solver of the compressible flow equations. The numerical method employs Linear Reconstructions of the primitive variables based on the least-squares approach of Barth. The standard Smagorinsky model is adopted as the subgrid term. The artificial viscosity inherent to the spatial discretization is maintained as low as possible reducing the dissipative contribution embedded in the approximate Riemann solver to the minimum necessary. Comparisons are also discussed with the results obtained using the implicit LES procedure. Two canonical test-cases are described: a fully developed pipe flow at a bulk Reynolds number Reb = 44 × 103 based on the pipe diameter, and a confined rotor-stator flow at the rotational Reynolds number ReΩ = 4 × 105 based on the outer radius. In both cases the mean flow and the turbulent statistics agree well with existing DNS or experimental data.
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LES of Wall Bounded Turbulent Flows Using Unstructured Linear Reconstruction Techniques
Volume 2B: Turbomachinery, 2014Co-Authors: Dario Amirante, Nicholas J. HillsAbstract:Large-Eddy Simulations of wall bounded, low Mach number turbulent flows are conducted using an unstructured finite-volume solver of the compressible flow equations. The numerical method employs Linear Reconstructions of the primitive variables based on the least-squares approach of Barth. The standard Smagorinsky model is adopted as the subgrid term. The artificial viscosity inherent to the spatial discretization is maintained as low as possible reducing the dissipative contribution embedded in the approximate Riemann solver to the minimum necessary. Comparisons are also discussed with the results obtained using the implicit LES procedure.Two canonical test-cases are described: a fully developed pipe flow at a bulk Reynolds number Reb = 44 × 103 based on the pipe diameter, and a confined rotor-stator flow at the rotational Reynolds number ReΩ = 4 × 105 based on the outer radius. In both cases the mean flow and the turbulent statistics agree well with existing DNS or experimental data.Copyright © 2014 by Rolls-Royce plc
Qiao Luan - One of the best experts on this subject based on the ideXlab platform.
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video summarization based on nonnegative Linear Reconstruction
International Conference on Multimedia and Expo, 2014Co-Authors: Qiao Luan, Mingli Song, Chu Yee Liau, Zicheng Liu, Mingting SunAbstract:With the development of imaging techniques and the Internet, it is hard to effectively and efficiently manage, index and store large amounts of videos. Video summarization appears to this need by obtaining useful information in videos. Recently, the Reconstruction concept has been introduced into video summarization. However, most of these existing Reconstruction methods where a dictionary of key frames is selected to reconstruct the original video best might come up with redundant information. In this paper, we propose a novel framework named Video Summarization based on Nonnegative Linear Reconstruction (VSNLR) which allows only additive, not subtractive, Linear combinations. Our approach consists of two core steps: (1) we detect the shot boundary and choose a representative frame for every shot, then all the representative frames form the candidate set; (2) for every frame in the candidate set, VSNLR selects related frames to reconstruct the given frame by using the nonnegative Linear Reconstruction function. The key frames are selected by minimizing the sum of Reconstruction errors. Experiments on a dataset and comparison to the state-of-art method demonstrate our advantage.
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ICME - Video Summarization based on Nonnegative Linear Reconstruction
2014 IEEE International Conference on Multimedia and Expo (ICME), 2014Co-Authors: Qiao Luan, Mingli Song, Chu Yee Liau, Zicheng Liu, Mingting SunAbstract:With the development of imaging techniques and the Internet, it is hard to effectively and efficiently manage, index and store large amounts of videos. Video summarization appears to this need by obtaining useful information in videos. Recently, the Reconstruction concept has been introduced into video summarization. However, most of these existing Reconstruction methods where a dictionary of key frames is selected to reconstruct the original video best might come up with redundant information. In this paper, we propose a novel framework named Video Summarization based on Nonnegative Linear Reconstruction (VSNLR) which allows only additive, not subtractive, Linear combinations. Our approach consists of two core steps: (1) we detect the shot boundary and choose a representative frame for every shot, then all the representative frames form the candidate set; (2) for every frame in the candidate set, VSNLR selects related frames to reconstruct the given frame by using the nonnegative Linear Reconstruction function. The key frames are selected by minimizing the sum of Reconstruction errors. Experiments on a dataset and comparison to the state-of-art method demonstrate our advantage.
Dario Amirante - One of the best experts on this subject based on the ideXlab platform.
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Large-Eddy Simulations of Wall Bounded Turbulent Flows Using Unstructured Linear Reconstruction Techniques
Journal of Turbomachinery, 2015Co-Authors: Dario Amirante, Nicholas J. HillsAbstract:Large-eddy simulations (LES) of wall bounded, low Mach number turbulent flows are conducted using an unstructured finite-volume solver of the compressible flow equations. The numerical method employs Linear Reconstructions of the primitive variables based on the least-squares approach of Barth. The standard Smagorinsky model is adopted as the subgrid term. The artificial viscosity inherent to the spatial discretization is maintained as low as possible reducing the dissipative contribution embedded in the approximate Riemann solver to the minimum necessary. Comparisons are also discussed with the results obtained using the implicit LES (ILES) procedure. Two canonical test-cases are described: a fully developed pipe flow at a bulk Reynolds number Reb = 44 × 103 based on the pipe diameter, and a confined rotor–stator flow at the rotational Reynolds number ReΩ = 4 × 105 based on the outer radius. In both cases, the mean flow and the turbulent statistics agree well with existing direct numerical simulations (DNS) or experimental data.
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LES OF WALL BOUNDED TURBULENT FLOWS USING UNSTRUCTURED Linear Reconstruction TECHNIQUES
Volume 2B: Turbomachinery, 2014Co-Authors: Dario Amirante, Nicholas J. HillsAbstract:Large-Eddy Simulations of wall bounded, low Mach number turbulent flows are conducted using an unstructured finite-volume solver of the compressible flow equations. The numerical method employs Linear Reconstructions of the primitive variables based on the least-squares approach of Barth. The standard Smagorinsky model is adopted as the subgrid term. The artificial viscosity inherent to the spatial discretization is maintained as low as possible reducing the dissipative contribution embedded in the approximate Riemann solver to the minimum necessary. Comparisons are also discussed with the results obtained using the implicit LES procedure. Two canonical test-cases are described: a fully developed pipe flow at a bulk Reynolds number Reb = 44 × 103 based on the pipe diameter, and a confined rotor-stator flow at the rotational Reynolds number ReΩ = 4 × 105 based on the outer radius. In both cases the mean flow and the turbulent statistics agree well with existing DNS or experimental data.
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LES of Wall Bounded Turbulent Flows Using Unstructured Linear Reconstruction Techniques
Volume 2B: Turbomachinery, 2014Co-Authors: Dario Amirante, Nicholas J. HillsAbstract:Large-Eddy Simulations of wall bounded, low Mach number turbulent flows are conducted using an unstructured finite-volume solver of the compressible flow equations. The numerical method employs Linear Reconstructions of the primitive variables based on the least-squares approach of Barth. The standard Smagorinsky model is adopted as the subgrid term. The artificial viscosity inherent to the spatial discretization is maintained as low as possible reducing the dissipative contribution embedded in the approximate Riemann solver to the minimum necessary. Comparisons are also discussed with the results obtained using the implicit LES procedure.Two canonical test-cases are described: a fully developed pipe flow at a bulk Reynolds number Reb = 44 × 103 based on the pipe diameter, and a confined rotor-stator flow at the rotational Reynolds number ReΩ = 4 × 105 based on the outer radius. In both cases the mean flow and the turbulent statistics agree well with existing DNS or experimental data.Copyright © 2014 by Rolls-Royce plc
Jianping Gou - One of the best experts on this subject based on the ideXlab platform.
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Representation-based classification methods with enhanced Linear Reconstruction measures for face recognition
Computers & Electrical Engineering, 2019Co-Authors: Jianping Gou, Jun Song, Shaoning Zeng, Yun-hao YuanAbstract:Abstract Representation-based classification (RBC) methods have recently been the promising pattern recognition technologies for object recognition. The representation coefficients of RBC as the Linear Reconstruction measure (LRM) can be well used for classifying objects. In this article, we propose two enhanced Linear Reconstruction measure-based classification methods based on the sparsity-augmented collaborative representation-based classification method (SA-CRC). One is the weighted enhancement Linear Reconstruction measure-based classification method (WELRMC) that introduces data localities into SA-CRC. Another is the two-phase weighted enhancement Linear Reconstruction measure-based classification method (TPWELRMC) that integrates both the coarse and fine representations into SA-CRC. To demonstrate the effectiveness of the proposed methods, experiments are conducted on several public face databases in comparison with the state-of-the-art representation-based classification methods. The experimental results show that the proposed methods significantly outperform the competing RBC methods.
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two phase Linear Reconstruction measure based classification for face recognition
Information Sciences, 2018Co-Authors: Jianping Gou, David Zhang, Qirong Mao, Yongzhao ZhanAbstract:Abstract In this article we propose several two-phase representation-based classification (RBC) methods that are inspired by the idea of the two-phase test sample sparse representation (TPTSR) method with L 2 -norm. We first introduce two simple extensions of TPTSR using L 1 -norm alone and the combination of L 1 -norm and L 2 -norm, respectively. We then propose two-phase Linear Reconstruction measure-based classification (TPLRMC) by adopting the Linear Reconstruction measure (LRM). Decomposing each feature sample as a weighted Linear combination of the other feature samples, TPLRMC can measure the similarities between any pairs of feature samples. The Linear Reconstruction coefficients can capture the feature’s neighborhood structure that is hidden in data. Thus, these coefficients with L p -norm regularization can be used as good similarity measures between samples and the test ones in classifier design of TPLRMC to enhance discriminative capability. In regard to the classification procedure, TPLRMC first coarsely searches K nearest neighbors for a given query sample with LRM, then finely represents the query sample as a Linear combination of the chosen K nearest neighbors, and finally uses LRM to perform classification. The experimental results on six face recognition databases and two object recognition databases demonstrate that the proposed methods outperform the competitors used in the experiments.
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VCIP - Weighted Two-Phase Linear Reconstruction Measure-based Classification
2018 IEEE Visual Communications and Image Processing (VCIP), 2018Co-Authors: Jianping Gou, Jun Song. Heping Song, Liangjun WangAbstract:Linear Reconstruction measure (LRM) is a promising similarity measure of data. In this paper, we consider the locality of data in LRM, and propose weighted two-phase Linear Reconstruction measure-based classification (WTPLRMC). In WTPLRMC, the first phase determines the representative training samples from all training samples by LRM, and the second phase constrains the Linear Reconstruction coefficients of the chosen representative training samples in first phase using the locality of data, which is reflected by the similarity weights between each test sample and the representative training samples. The effectiveness of the proposed WTPLRMC is well demonstrated on some benchmark face databases with satisfactory classification results.
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PCM (1) - Two-Phase Representation Based Classification
Lecture Notes in Computer Science, 2015Co-Authors: Jianping Gou, Qirong Mao, Yongzhao Zhan, Xiang-jun Shen, Liangjun WangAbstract:In this paper, we propose the two-phase representation based classification called the two-phase Linear Reconstruction measure based classification (TPLRMC). It is inspired from the fact that the Linear Reconstruction measure (LRM) gauges the similarities among feature samples by decomposing each feature sample as a liner combination of the other feature samples with \(L_{p}\)-norm regularization. Since the Linear Reconstruction coefficients can fully reveal the feature’s neighborhood structure that is hidden in the data, the similarity measures among the training samples and the query sample are well provided in classifier design. In TPLRMC, it first coarsely seeks the K nearest neighbors for the query sample with LRM, and then finely represents the query sample as the Linear combination of the determined K nearest neighbors and uses LRM to perform classification. The experimental results on face databases show that TPLRMC can significantly improve the classification performance.