检索结果-内蒙古大学图书馆

A multi-kernel framework with nonparallel support vector machine

NEUROCOMPUTING 2017年 266卷 226-238页

作者： Tang, Jingjing Tian, Yingjie Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China

Multiple kernel learning (MKL) serves as an attractive research direction in current kernel machine learning field. It can flexibly process diverse characteristics of patterns such as heterogeneous information or irregular data, non-flat distribution of high-dimensional samples etc. The existing MM. models are usually built on SVM. However, there is still potential to improve the performance of MKL instead of learning based on SVM. Nonparallel support vector machine (NPSVM), as a novel clasifier, pursues two nonparallel proximal hyperplanes with several incomparable advantages over the state-of-the-art classifiers. In this paper, we propose a new model termed as MKNPSVM for classification. By integrating NPSVM into the MKL framework, MKNPSVM inherits the advantages of them and opens a new perspective to extend NPSVM to the MKL field. To solve MKNPSVM efficiently, we provide an alternating optimization algorithm (Alter-MKNPSVM for short) as the solution. We theoretically analyze the performance of MKNPSVM from three viewpoints: the generalization capability analysis, the convergence analysis and the comparisons with NPSVM and MKL. Experimental results on eighteen publicly available UCI data sets confirm the effectiveness of our method. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Multiple kernel learning Nonparallel support vector machine Alternating optimization

来源：评论

学校读者我要写书评

暂无评论

VIDEO SALIENCY PREDICTION THROUGH MACHINE LEARNING WITH SEMANTIC INFORMATION

VIDEO SALIENCY PREDICTION THROUGH MACHINE LEARNING WITH SEMA...

引用

IEEE China Summit & International Conference on Signal and Information Processing

作者： Fu, Xiaohui Su, Li Qin, Lei Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Inst Comp Technol Key Lab Intelligent Informat Proc Beijing Peoples R China

ISBN: (纸本)9781479919482

Saliency prediction is valuable in many video applications, such as intelligent retrieval, advertisement design and delivering, video coding and video summarization generating. Although image saliency is well explored, less works have been done on videos. Compared to images, the semantic orientation is more obvious for video saliency. In this paper, we propose a method to predict video saliency by introducing semantic information. Different from existing approaches, we simultaneously consider the bottom -up and top -down factors in a machine learning framework and utilize a semantic object learning model to compute the semantic related saliency map. The proposed method is tested on two datasets. The experiment results show that the proposed method keeps higher consistent with human's gaze tracks data on various video contents. Furthermore, the computation efficiency is also improved as we don't need to process every pixel of each frame during prediction features extraction.

关键词： video saliency machine learning semantic orientation information bottom-up top-down

来源：评论

学校读者我要写书评

暂无评论

Partial Fingerprint Matching via Phase-Only Correlation and Deep Convolutional Neural Network 24th

Partial Fingerprint Matching via Phase-Only Correlation and ...

引用

24th International Conference on Neural Information Processing (ICONIP)

作者： Qin, Jin Tang, Siqi Han, Congying Guo, Tiande Univ Chinese Acad Sci Beijing Peoples R China UCAS Sch Math Sci Beijing Peoples R China UCAS Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

ISBN: (纸本)9783319701363;9783319701356

A major approach for fingerprint matching today is based on minutiae. However, due to the lack of minutiae, their accuracy degrades significantly for partial-to-partial matching. We propose a novel matching algorithm that makes full use of the distinguishing information in partial fingerprint images. Our model employs the Phase-Only Correlation (POC) function to coarsely assign two fingerprints. Then we use a deep convolutional neural network (CNN) with spatial pyramid pooling to measure the similarity of the overlap areas. Experiments indicate that our algorithm has an excellent performance.

关键词： Partial fingerprint matching Phase-only correlation Polar Fourier transform Deep convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

YUN: A Fast Ground-to-air Cloud Image Recognition Framework 23

YUN: A Fast Ground-to-air Cloud Image Recognition Framework

引用

IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Cui, Zhenyu Liu, Ying Zhao, Wei Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

ISBN: (纸本)9781728103501

The recognition of cloud maps plays an important role in the field of meteorological prediction. The mainstream recognition of cloud maps mainly focuses on satellite cloud maps, while research on ground -to -air cloud maps is less. In this paper, we propose an analysis framework - YUN - to quickly locate and identify clouds in these maps. A Quick Cloud region Proposal(QCP) was designed to segment cloud maps to the greatest extent. Then we use the convolutional neural network to extract features of the cloud maps and use fully connected network to predict the type of cloud. In addition, a voting mechanism with a variety of methods has achieved good prediction results.

关键词： cloud region proposal convolutional neural network ground-to-air cloud maps

来源：评论

学校读者我要写书评

暂无评论

Algorithms for computing strong μ-bases for rational tensor product surfaces

引用

COMPUTER AIDED GEOMETRIC DESIGN 2017年 52-53卷 48-62页

作者： Shen, Li-Yong Goldman, Ron Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Rice Univ Dept Comp Sci 6100 Main StMS-132 Houston TX 77005 USA

Implicitizing rational surfaces is a fundamental computational task in Algorithmic Algebraic Geometry. Although the resultant of a mu-basis for a rational surface is guaranteed to contain the implicit equation of the surface as a factor, this resultant may also contain extraneous factors. Moreover, mu-bases for rational surfaces are, in general, notoriously difficult to compute. Here we develop fast algorithms to find mu-bases for rational tensor product surfaces whose resultants are guaranteed to be the implicit equation of the corresponding rational surface with no extraneous factors. We call these mu-bases strong mu-bases. Surfaces with strong mu-bases are relatively rare. We show how these strong mu-bases are related to the number of base points counting multiplicity of the corresponding surface parametrization. In addition, when the base points are simple, we provide tables of rational tensor product surfaces with strong mu-bases based on the bidegree of the rational surface and the number of base points of the parametrization. The bidegrees of the corresponding strong mu-bases are also listed in these tables. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Rational surface Base point Implicitization Moving plane Syzygy Strong mu-basis

来源：评论

学校读者我要写书评

暂无评论

Cross-Modal Retrieval Using Multiordered Discriminative Structured Subspace Learning

引用

IEEE TRANSACTIONS ON MULTIMEDIA 2017年第6期19卷 1220-1233页

作者： Zhang, Liang Ma, Bingpeng Li, Guorong Huang, Qingming Tian, Qi Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Texas San Antonio Dept Comp Sci San Antonio TX 78249 USA

This paper proposes a novel method for cross-modal retrieval. In addition to the traditional vector (text)-to-vector (image) framework, we adopt a matrix (text)-to-matrix (image) framework to faithfully characterize the structures of different feature spaces. Moreover, we propose a novel metric learning framework to learn a discriminative structured subspace, in which the underlying data distribution is preserved for ensuring a desirablemetric. Concretely, there are three steps for the proposed method. First, the multiorder statistics are used to represent images and texts for enriching the feature information. We jointly use the covariance (second-order), mean (first-order), and bags of visual (textual) features (zeroth-order) to characterize each image and text. Second, considering that the heterogeneous covariance matrices lie on the different Riemannian manifolds and the other features on the different Euclidean spaces, respectively, we propose a unified metric learning framework integrating multiple distance metrics, one for each order statistical feature. This framework preserves the underlying data distribution and exploits complementary information for better matching heterogeneous data. Finally, the similarity between the different modalities can be measured by transforming the multiorder statistical features to the common subspace. The performance of the proposed method over the previous methods has been demonstrated through the experiments on two public datasets.

关键词： Cross-modal retrieval documents and images multimedia

来源：评论

学校读者我要写书评

暂无评论

Generalized Semi-supervised and Structured Subspace Learning for Cross-Modal Retrieval

引用

IEEE TRANSACTIONS ON MULTIMEDIA 2018年第1期20卷 128-141页

Motivated by the fact that unlabeled data can be easily collected and help to exploit the correlations among different modalities, this paper proposes a novel method named generalized semi-supervised structured subspace learning (GSS-SL) for the task of cross-modal retrieval. First, to predict more relevant class labels for unlabeled data, we propose a label graph constraint that ensures the intrinsic geometric structures of different feature spaces consistent with that of label space. Second, considering that class labels directly reveal the semantic information of multimedia data, GSS-SL takes the label space as a linkage to model the correlations among different modalities. Concretely, the label graph constraint, label-linked loss function, and regularization are integrated into a joint minimization formulation to learn a discriminative common subspace. Finally, an efficient optimization algorithm is designed to alternately optimize multiple linear transformations for different modalities and update the class indicator matrices for unlabeled data. Furthermore, an arbitrary number of modalities can be solved in the proposed framework. Extensive experiments on three standard benchmark datasets demonstrate that GSS-SL outperforms previous methods on exploiting the correlations among different modalities.

关键词： Cross-modal retrieval documents and images semi-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Computing μ-bases from algebraic ruled surfaces

引用

COMPUTER AIDED GEOMETRIC DESIGN 2016年第0期46卷 125-130页

作者： Shen, Li-Yong Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Chinese Acad Sci Acad Math & Syst Sci KLMM Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

We find a mu-basis for a rational ruled surface, starting from its implicit representation. A parametrization for this ruled surface is then deduced form this mu-basis. This parametrization does not have any non-generic base points and its directrix has the lowest possible degree. A complete interchange graph is built for the algebraic equation, the mu-basis and the parametric equation of a rational ruled surface. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Rational ruled surface Planar directrix mu-Basis Parametrization

来源：评论

学校读者我要写书评

暂无评论

Alternating Direction Method of Multipliers for Nonparallel Support Vector Machines 15

Alternating Direction Method of Multipliers for Nonparallel ...

引用

IEEE 15th International Conference on data mining Workshops (ICDMW)

作者： Shen, Xin Niu, Lingfeng Tian, Yingjie Shi, Yong Univ Chinese Acad Sci Coll Math Sci Beijing 100049 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China

ISBN: (纸本)9781467384933

Recently, a novel nonparallel support vector machine(NPSVM) is proposed by Tian et al, which has several attracting advantages over its predecessors. A sequential minimal optimization algorithm(SMO) has already been provided to solve the dual form of NPSVM. Different from the existing work, we present a new strategy to solve the primal form of NPSVM in this paper. Our algorithm is designed in the framework of the alternating direction method of multipliers(ADMM), which is well suited to distributed convex optimization. Although the closed-form solution of each step can be written out directly, in order to be able to handle problems with a very large number of features or training examples, we propose to solve the underlying linear equation systems proximally by the conjugate gradient method. Experiments are carried out on several data sets. Numerical results indeed demonstrate the effectiveness of our method.

关键词： alternating direction method of multipliers conjugate gradient method nonparallel support vector machine support vector machine

来源：评论

学校读者我要写书评

暂无评论

Generalized Zero-Shot Video Classification via Generative Adversarial Networks 20

Generalized Zero-Shot Video Classification via Generative Ad...

引用

28th ACM International Conference on Multimedia (MM)

作者： Hong, Mingyao Li, Guorong Zhang, Xinfeng Huang, Qingming UCAS Sch Comp Sci & Technol Beijing Peoples R China UCAS Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Chinese Acad Sci ICT Key Lab Intelligent Informat Proc Beijing Peoples R China

ISBN: (纸本)9781450379885

Zero-shot learning (ZSL) is to classify images according to detailed attribute annotations into new categories that are unseen during the training stage. Generalized zero-shot learning (GZSL) adds seen categories to the test samples. Since the learned classifier has inherent bias against seen categories, GZSL is more challenging than traditional ZSL. However, at present, there is no detailed attribute description dataset for video classification. Therefore, the current zero-shot video classification problem is based on the synthesis of generative adversarial networks trained on seen-class features into unseen-class features for ZSL classification. In order to solve this problem, we propose a description text dataset based on the UCF101 action recognition dataset. To the best of our knowledge, this is the first work to add description of the classes to zero-shot video classification. We propose a new loss function that combines visual features with textual features. We extract text features from the proposed text data set, and constrain the process of generating synthetic features based on the principle that videos with similar text types should be similar. Our method reapplies the traditional zero-shot learning idea to video classification. From the experimental point of view, our proposed dataset and method have a positive impact on the generalized zero-shot video classification.

关键词： zero-shot learning datasets video classification text description

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：