The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Jinghao Xue - One of the best experts on this subject based on the ideXlab platform.
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constrained mutual Convex Cone method for image set based recognition
Pattern Recognition, 2021Co-Authors: Naoya Sogi, Rui Zhu, Jinghao Xue, Kazuhiro FukuiAbstract:In this paper, we propose a method for image-set classification based on Convex Cone models. Image set classification aims to classify a set of images, which were usually obtained from video frames or multi-view cameras, into a target object. To accurately and stably classify a set, it is essential to represent structural information of the set accurately. There are various representative image features, such as histogram based features, HLAC, and Convolutional Neural Network (CNN) features. We should note that most of them have non-negativity and thus can be effectively represented by a Convex Cone. This leads us to introduce the Convex Cone representation to image-set classification. To establish a Convex Cone based framework, we mathematically define multiple angles between two Convex Cones, and then define the geometric similarity between the Cones using the angles. Moreover, to enhance the framework, we introduce a discriminant space that maximizes the between-class variance (gaps) and minimizes the within-class variance of the projected Convex Cones onto the discriminant space, similar to the Fisher discriminant analysis. Finally, the classification is performed based on the similarity between projected Convex Cones. The effectiveness of the proposed method is demonstrated experimentally by using five databases: CMU PIE dataset, ETH-80, CMU Motion of Body dataset, Youtube Celebrity dataset, and a private database of multi-view hand shapes.
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a novel separating hyperplane classification framework to unify nearest class model methods for high dimensional data
IEEE Transactions on Neural Networks, 2020Co-Authors: Rui Zhu, Ziyu Wang, Naoya Sogi, Kazuhiro Fukui, Jinghao XueAbstract:In this article, we establish a novel separating hyperplane classification (SHC) framework to unify three nearest-class-model methods for high-dimensional data: the nearest subspace method (NSM), the nearest Convex hull method (NCHM), and the nearest Convex Cone method (NCCM). Nearest-class-model methods are an important paradigm for the classification of high-dimensional data. We first introduce the three nearest-class-model methods and then conduct dual analysis for theoretically investigating them, to understand deeply their underlying classification mechanisms. A new theorem for the dual analysis of NCCM is proposed in this article by discovering the relationship between a Convex Cone and its polar Cone. We then establish the new SHC framework to unify the nearest-class-model methods based on the theoretical results. One important application of this new SHC framework is to help explain empirical classification results: why one class model has a better performance than others on certain data sets. Finally, we propose a new nearest-class-model method, the soft NCCM, under the novel SHC framework to solve the overlapping class model problem. For illustrative purposes, we empirically demonstrate the significance of our SHC framework and the soft NCCM through two types of typical real-world high-dimensional data: the spectroscopic data and the face image data.
Rui Zhu - One of the best experts on this subject based on the ideXlab platform.
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constrained mutual Convex Cone method for image set based recognition
Pattern Recognition, 2021Co-Authors: Naoya Sogi, Rui Zhu, Jinghao Xue, Kazuhiro FukuiAbstract:In this paper, we propose a method for image-set classification based on Convex Cone models. Image set classification aims to classify a set of images, which were usually obtained from video frames or multi-view cameras, into a target object. To accurately and stably classify a set, it is essential to represent structural information of the set accurately. There are various representative image features, such as histogram based features, HLAC, and Convolutional Neural Network (CNN) features. We should note that most of them have non-negativity and thus can be effectively represented by a Convex Cone. This leads us to introduce the Convex Cone representation to image-set classification. To establish a Convex Cone based framework, we mathematically define multiple angles between two Convex Cones, and then define the geometric similarity between the Cones using the angles. Moreover, to enhance the framework, we introduce a discriminant space that maximizes the between-class variance (gaps) and minimizes the within-class variance of the projected Convex Cones onto the discriminant space, similar to the Fisher discriminant analysis. Finally, the classification is performed based on the similarity between projected Convex Cones. The effectiveness of the proposed method is demonstrated experimentally by using five databases: CMU PIE dataset, ETH-80, CMU Motion of Body dataset, Youtube Celebrity dataset, and a private database of multi-view hand shapes.
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a novel separating hyperplane classification framework to unify nearest class model methods for high dimensional data
IEEE Transactions on Neural Networks, 2020Co-Authors: Rui Zhu, Ziyu Wang, Naoya Sogi, Kazuhiro Fukui, Jinghao XueAbstract:In this article, we establish a novel separating hyperplane classification (SHC) framework to unify three nearest-class-model methods for high-dimensional data: the nearest subspace method (NSM), the nearest Convex hull method (NCHM), and the nearest Convex Cone method (NCCM). Nearest-class-model methods are an important paradigm for the classification of high-dimensional data. We first introduce the three nearest-class-model methods and then conduct dual analysis for theoretically investigating them, to understand deeply their underlying classification mechanisms. A new theorem for the dual analysis of NCCM is proposed in this article by discovering the relationship between a Convex Cone and its polar Cone. We then establish the new SHC framework to unify the nearest-class-model methods based on the theoretical results. One important application of this new SHC framework is to help explain empirical classification results: why one class model has a better performance than others on certain data sets. Finally, we propose a new nearest-class-model method, the soft NCCM, under the novel SHC framework to solve the overlapping class model problem. For illustrative purposes, we empirically demonstrate the significance of our SHC framework and the soft NCCM through two types of typical real-world high-dimensional data: the spectroscopic data and the face image data.
Kazuhiro Fukui - One of the best experts on this subject based on the ideXlab platform.
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constrained mutual Convex Cone method for image set based recognition
Pattern Recognition, 2021Co-Authors: Naoya Sogi, Rui Zhu, Jinghao Xue, Kazuhiro FukuiAbstract:In this paper, we propose a method for image-set classification based on Convex Cone models. Image set classification aims to classify a set of images, which were usually obtained from video frames or multi-view cameras, into a target object. To accurately and stably classify a set, it is essential to represent structural information of the set accurately. There are various representative image features, such as histogram based features, HLAC, and Convolutional Neural Network (CNN) features. We should note that most of them have non-negativity and thus can be effectively represented by a Convex Cone. This leads us to introduce the Convex Cone representation to image-set classification. To establish a Convex Cone based framework, we mathematically define multiple angles between two Convex Cones, and then define the geometric similarity between the Cones using the angles. Moreover, to enhance the framework, we introduce a discriminant space that maximizes the between-class variance (gaps) and minimizes the within-class variance of the projected Convex Cones onto the discriminant space, similar to the Fisher discriminant analysis. Finally, the classification is performed based on the similarity between projected Convex Cones. The effectiveness of the proposed method is demonstrated experimentally by using five databases: CMU PIE dataset, ETH-80, CMU Motion of Body dataset, Youtube Celebrity dataset, and a private database of multi-view hand shapes.
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a novel separating hyperplane classification framework to unify nearest class model methods for high dimensional data
IEEE Transactions on Neural Networks, 2020Co-Authors: Rui Zhu, Ziyu Wang, Naoya Sogi, Kazuhiro Fukui, Jinghao XueAbstract:In this article, we establish a novel separating hyperplane classification (SHC) framework to unify three nearest-class-model methods for high-dimensional data: the nearest subspace method (NSM), the nearest Convex hull method (NCHM), and the nearest Convex Cone method (NCCM). Nearest-class-model methods are an important paradigm for the classification of high-dimensional data. We first introduce the three nearest-class-model methods and then conduct dual analysis for theoretically investigating them, to understand deeply their underlying classification mechanisms. A new theorem for the dual analysis of NCCM is proposed in this article by discovering the relationship between a Convex Cone and its polar Cone. We then establish the new SHC framework to unify the nearest-class-model methods based on the theoretical results. One important application of this new SHC framework is to help explain empirical classification results: why one class model has a better performance than others on certain data sets. Finally, we propose a new nearest-class-model method, the soft NCCM, under the novel SHC framework to solve the overlapping class model problem. For illustrative purposes, we empirically demonstrate the significance of our SHC framework and the soft NCCM through two types of typical real-world high-dimensional data: the spectroscopic data and the face image data.
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a method based on Convex Cone model for image set classification with cnn features
International Joint Conference on Neural Network, 2018Co-Authors: Naoya Sogi, Taku Nakayama, Kazuhiro FukuiAbstract:In this paper, we propose a method for image-set classification based on Convex Cone models, focusing on the effectiveness of convolutional neural network (CNN) features as its input. CNN feature has non-negative values when using the rectified linear unit as an activation function. This naturally leads us to model a set of CNN features by a Convex Cone and measure the geometrical similarity of Convex Cones in classification. To achieve this framework, we define sequentially multiple angles between two Convex Cones by repeating the alternating least square method, and then define the geometrical similarity between the Cones by using the obtained angles. Moreover, to enhance our method, we introduce a discriminant space, which maximizes the between-class variance (gaps) and minimizes the within-class variance of the projected Convex Cones onto the discriminant space, like Fisher discriminant analysis. Finally, the classification is conducted by measuring the similarity between projected Convex Cones. The effectiveness of the proposed method is demonstrated through evaluation experiments on a private database of a multi-view hand shape dataset, and two public databases.
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a method based on Convex Cone model for image set classification with cnn features
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Naoya Sogi, Taku Nakayama, Kazuhiro FukuiAbstract:In this paper, we propose a method for image-set classification based on Convex Cone models, focusing on the effectiveness of convolutional neural network (CNN) features as inputs. CNN features have non-negative values when using the rectified linear unit as an activation function. This naturally leads us to model a set of CNN features by a Convex Cone and measure the geometric similarity of Convex Cones for classification. To establish this framework, we sequentially define multiple angles between two Convex Cones by repeating the alternating least squares method and then define the geometric similarity between the Cones using the obtained angles. Moreover, to enhance our method, we introduce a discriminant space, maximizing the between-class variance (gaps) and minimizes the within-class variance of the projected Convex Cones onto the discriminant space, similar to a Fisher discriminant analysis. Finally, classification is based on the similarity between projected Convex Cones. The effectiveness of the proposed method was demonstrated experimentally using a private, multi-view hand shape dataset and two public databases.
Naoya Sogi - One of the best experts on this subject based on the ideXlab platform.
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constrained mutual Convex Cone method for image set based recognition
Pattern Recognition, 2021Co-Authors: Naoya Sogi, Rui Zhu, Jinghao Xue, Kazuhiro FukuiAbstract:In this paper, we propose a method for image-set classification based on Convex Cone models. Image set classification aims to classify a set of images, which were usually obtained from video frames or multi-view cameras, into a target object. To accurately and stably classify a set, it is essential to represent structural information of the set accurately. There are various representative image features, such as histogram based features, HLAC, and Convolutional Neural Network (CNN) features. We should note that most of them have non-negativity and thus can be effectively represented by a Convex Cone. This leads us to introduce the Convex Cone representation to image-set classification. To establish a Convex Cone based framework, we mathematically define multiple angles between two Convex Cones, and then define the geometric similarity between the Cones using the angles. Moreover, to enhance the framework, we introduce a discriminant space that maximizes the between-class variance (gaps) and minimizes the within-class variance of the projected Convex Cones onto the discriminant space, similar to the Fisher discriminant analysis. Finally, the classification is performed based on the similarity between projected Convex Cones. The effectiveness of the proposed method is demonstrated experimentally by using five databases: CMU PIE dataset, ETH-80, CMU Motion of Body dataset, Youtube Celebrity dataset, and a private database of multi-view hand shapes.
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a novel separating hyperplane classification framework to unify nearest class model methods for high dimensional data
IEEE Transactions on Neural Networks, 2020Co-Authors: Rui Zhu, Ziyu Wang, Naoya Sogi, Kazuhiro Fukui, Jinghao XueAbstract:In this article, we establish a novel separating hyperplane classification (SHC) framework to unify three nearest-class-model methods for high-dimensional data: the nearest subspace method (NSM), the nearest Convex hull method (NCHM), and the nearest Convex Cone method (NCCM). Nearest-class-model methods are an important paradigm for the classification of high-dimensional data. We first introduce the three nearest-class-model methods and then conduct dual analysis for theoretically investigating them, to understand deeply their underlying classification mechanisms. A new theorem for the dual analysis of NCCM is proposed in this article by discovering the relationship between a Convex Cone and its polar Cone. We then establish the new SHC framework to unify the nearest-class-model methods based on the theoretical results. One important application of this new SHC framework is to help explain empirical classification results: why one class model has a better performance than others on certain data sets. Finally, we propose a new nearest-class-model method, the soft NCCM, under the novel SHC framework to solve the overlapping class model problem. For illustrative purposes, we empirically demonstrate the significance of our SHC framework and the soft NCCM through two types of typical real-world high-dimensional data: the spectroscopic data and the face image data.
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a method based on Convex Cone model for image set classification with cnn features
International Joint Conference on Neural Network, 2018Co-Authors: Naoya Sogi, Taku Nakayama, Kazuhiro FukuiAbstract:In this paper, we propose a method for image-set classification based on Convex Cone models, focusing on the effectiveness of convolutional neural network (CNN) features as its input. CNN feature has non-negative values when using the rectified linear unit as an activation function. This naturally leads us to model a set of CNN features by a Convex Cone and measure the geometrical similarity of Convex Cones in classification. To achieve this framework, we define sequentially multiple angles between two Convex Cones by repeating the alternating least square method, and then define the geometrical similarity between the Cones by using the obtained angles. Moreover, to enhance our method, we introduce a discriminant space, which maximizes the between-class variance (gaps) and minimizes the within-class variance of the projected Convex Cones onto the discriminant space, like Fisher discriminant analysis. Finally, the classification is conducted by measuring the similarity between projected Convex Cones. The effectiveness of the proposed method is demonstrated through evaluation experiments on a private database of a multi-view hand shape dataset, and two public databases.
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a method based on Convex Cone model for image set classification with cnn features
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Naoya Sogi, Taku Nakayama, Kazuhiro FukuiAbstract:In this paper, we propose a method for image-set classification based on Convex Cone models, focusing on the effectiveness of convolutional neural network (CNN) features as inputs. CNN features have non-negative values when using the rectified linear unit as an activation function. This naturally leads us to model a set of CNN features by a Convex Cone and measure the geometric similarity of Convex Cones for classification. To establish this framework, we sequentially define multiple angles between two Convex Cones by repeating the alternating least squares method and then define the geometric similarity between the Cones using the obtained angles. Moreover, to enhance our method, we introduce a discriminant space, maximizing the between-class variance (gaps) and minimizes the within-class variance of the projected Convex Cones onto the discriminant space, similar to a Fisher discriminant analysis. Finally, classification is based on the similarity between projected Convex Cones. The effectiveness of the proposed method was demonstrated experimentally using a private, multi-view hand shape dataset and two public databases.
Cheini Chang - One of the best experts on this subject based on the ideXlab platform.
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geometric Convex Cone volume analysis
Data Compression Communications and Processing, 2016Co-Authors: Cheini ChangAbstract:Convexity is a major concept used to design and develop endmember finding algorithms (EFAs). For abundance unconstrained techniques, Pixel Purity Index (PPI) and Automatic Target Generation Process (ATGP) which use Orthogonal Projection (OP) as a criterion, are commonly used method. For abundance partially constrained techniques, Convex Cone Analysis is generally preferred which makes use of Convex Cones to impose Abundance Non-negativity Constraint (ANC). For abundance fully constrained N-FINDR and Simplex Growing Algorithm (SGA) are most popular methods which use simplex volume as a criterion to impose ANC and Abundance Sum-to-one Constraint (ASC). This paper analyze an issue encountered in volume calculation with a hyperplane introduced to illustrate an idea of bounded Convex Cone. Geometric Convex Cone Volume Analysis (GCCVA) projects the boundary vectors of a Convex Cone orthogonally on a hyperplane to reduce the effect of background signatures and a geometric volume approach is applied to address the issue arose from calculating volume and further improve the performance of Convex Cone-based EFAs.
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Convex Cone volume analysis for finding endmembers in hyperspectral imagery
International Journal of Computational Science and Engineering, 2016Co-Authors: Cheini Chang, Wei Xiong, Shihyu ChenAbstract:This paper presents a new approach, called Convex Cone volume analysis (CCVA), which can be considered as a partially constrained-abundance (abundance non-negativity constraint) technique to find endmembers. It can be shown that finding the maximal volume of a Convex Cone in the original data space is equivalent to finding the maximal volume of a simplex in a hyperplane. As a result, the CCVA can take advantage of many recently developed fast computational algorithms developed for N-FINDR to derive their counterparts for CCVA.
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partially geometric constrained sequential endmember finding Convex Cone volume analysis
2016Co-Authors: Cheini ChangAbstract:N-FINDR discussed in Chap. 6 uses Simplex Volume (SV) as a criterion to find endmembers which specify the vertices of a simplex with maximal SV. A simplex can be considered as a Convex set within which all data sample vectors are fully constrained by its vertices via linear Convexity. From a Linear Spectral Mixture Analysis (LSMA) viewpoint, the data sample vectors within a simplex can be linearly mixed by its vertices with full abundance constraints, Abundance Sum-to-one Constraint (ASC) and Abundance Non-negativity Constraint (ANC). This chapter presents an approach, called called Convex Cone Volume Analysis (CCVA) developed by Chang et al. (2016) that uses one fewer abundance constraint by only imposing ANC without ASC for finding endmembers. It is a partially abundance-constrained (more specifically, ANC) technique which implements Convex Cone Volume (CCV) as a criterion instead of SV used by N-FINDR. As shown in this chapter, finding the maximal volume of a Convex Cone in the original data space is equivalent to finding the maximal volume of a simplex formed by the projection of the Convex Cone on a specific hyperplane, referred to as Convex Cone Projection (CCP) whose dimensionality is reduced by one from the original data dimensionality. This makes sense because a simplex requires an additional ASC imposed on its Convexity structure and projecting a Convex Cone on a hyperplane is equivalent to imposing ASC on CCP, which is actually a simplex on a hyperplane. As a result, CCVA can take full advantage of whatever is developed for N-FINDR in Chap. 6 to derive its counterpart for CCVA.
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partially geometric constrained progressive endmember finding growing Convex Cone volume analysis
2016Co-Authors: Cheini ChangAbstract:Chapter 7 presents a Convex Cone Volume Analysis (CCVA) approach developed by Chang et al. (2016) to finding endmembers which maximizes Convex Cone volumes for a given fixed number of Convex Cone vertices in the same way that N-FINDR maximizes simplex volumes in Chap. 6 for a given fixed number of simplex vertices. Its main idea is to project a Convex Cone onto a hyperplane so that the projected Convex Cone becomes a simplex. With this advantage, what can be derived from N-FINDR in Chap. 6 can also be applied to CCVA in Chap. 7. To reduce computational complexity and relieve the computing time required by N-FINDR, a Simplex Growing Analysis (SGA) approach developed by Chang et al. (2006) is further discussed in Chap. 10. More specifically, instead of working on fixed-size simplexes as does N-FINDR, SGA grows simplexes to find maximal volumes of growing simplexes by adding new vertices one at a time. Because CCVA can be derived from N-FINDR, it is expected that a similar approach can also be applied to SGA. This chapter develops a Growing Convex Cone Volume Analysis (GCCVA) approach, which is a parallel theory to SGA and can be considered to be a progressive version of CCVA in the same way as SGA is developed in Chap. 10 as a progressive version of N-FINDR. Accordingly, what SGA is to N-FINDR is exactly what GCCVA is to CCVA.
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Convex Cone based endmember extraction for hyperspectral imagery
Proceedings of SPIE, 2010Co-Authors: Wei Xiong, Cheini Chang, Ching Tsorng Tsai, Ching Wen YangAbstract:N-finder algorithm (N-FINDR) is a simplex-based fully abundance constrained technique which is operated on the original data space. This paper presents an approach, Convex-Cone N-FINDR (CC N-FINDR) which combines N-FINDR with Convex Cone data obtained from the original data so as to improve the N-FINDR in computational complexity and performance. The same Convex Cone approach can be also applied to simplex growing algorithm (SGA) to derive a new Convex Cone-based growing algorithm (CCGA) which also improves the SGA in the same manner as it does for NFINDR. With success in CC N-FINDR and CCGA a similar treatment of using Convex Cone can be further used to improve any endmember extraction algorithm (EEA). Experimental results are included to demonstrate advantages of the Convex Cone-based EEAs over EEAs without using Convex Cone.
