检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

19 篇 会议

馆藏范围

19 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

12 篇 工学
- 6 篇 机械工程
- 6 篇 计算机科学与技术...
- 3 篇 电气工程
- 2 篇 控制科学与工程
- 1 篇 仪器科学与技术
- 1 篇 轻工技术与工程
- 1 篇 软件工程
1 篇 理学
- 1 篇 数学
- 1 篇 统计学（可授理学、...
1 篇 管理学
- 1 篇 图书情报与档案管...
1 篇 艺术学
- 1 篇 设计学（可授艺术学...

主题

6 篇 image retrieval
6 篇 pattern analysis
5 篇 kernel
4 篇 hidden markov mo...
4 篇 europe
3 篇 handwriting reco...
3 篇 visualization
3 篇 feature extracti...
3 篇 histograms
3 篇 accuracy
2 篇 rendering (compu...
2 篇 pattern classifi...
2 篇 vocabulary
2 篇 vectors
2 篇 pattern recognit...
2 篇 sampling methods
2 篇 image representa...
2 篇 text analysis
2 篇 image classifica...
2 篇 distributed comp...

机构

12 篇 textual and visu...
4 篇 xerox research c...
3 篇 computer vision ...
2 篇 computer vision ...
2 篇 computer vision ...
2 篇 textual and visu...
2 篇 computer vision ...
1 篇 computer science...
1 篇 laboratoire dinf...
1 篇 computer vision ...
1 篇 textual and visu...

作者

10 篇 florent perronni...
8 篇 perronnin floren...
3 篇 yan liu
3 篇 gordo albert
2 篇 marchesotti luca
2 篇 rodriguez-serran...
2 篇 liu yan
2 篇 csurka gabriela
2 篇 josé a. rodrígue...
2 篇 albert gordo
1 篇 jorge sanchez
1 篇 renders jean-mic...
1 篇 jean-michel rend...
1 篇 cifarelli claudi...
1 篇 jose a. rodrigue...
1 篇 rodríguez-serran...
1 篇 albert gordoa
1 篇 gemma sanchez
1 篇 ernest valveny
1 篇 jose a. rodrigue...

语言

19 篇 英文

检索条件"机构=Textual and Visual Pattern Analysis Xerox Research Centre Europe"

共 19 条记录，以下是11-20 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

A framework for visual saliency detection with applications to image thumbnailing

A framework for visual saliency detection with applications ...

引用

12th IEEE International Conference on Computer Vision

作者： Marchesotti, Luca Cifarelli, Claudio Csurka, Gabriela Textual and Visual Pattern Analysis (TVPA) Xerox Research Centre Europe (XRCE) France

ISBN: (纸本)9781424444199

We propose a novel framework for visual saliency detection based on a simple principle: images sharing their global visual appearances are likely to share similar salience. Assuming that an annotated image database is available, we first retrieve the most similar images to the target image;secondly, we build a simple classifier and we use it to generate saliency maps. Finally, we refine the maps and we extract thumbnails. We show that in spite of its simplicity, our framework outperforms state-of-the-art approaches. Another advantage is its ability to deal with visual pop-up and application/task-driven saliency, if appropriately annotated images are available.

关键词： visualization

来源：评论

学校读者我要写书评

暂无评论

Fisher kernels for handwritten word-spotting

Fisher kernels for handwritten word-spotting

引用

ICDAR2009 - 10th International Conference on Document analysis and Recognition

作者： Perronnin, Florent Rodriguez-Serrano, Jose A. Textual and Visual Pattern Analysis Xerox Research Centre Europe France Computer Vision Centre Universitat Autonoma de Barcelona Spain

ISBN: (纸本)9780769537252

The Fisher kernel is a generic framework which combines the benefits of generative and discriminative approaches to pattern classification. In this contribution, we propose to apply this framework to handwritten word-spotting. Given a word image and a keyword generative model, the idea is to generate a vector which describes how the parameters of the keyword model should be modified to best fit the word image. This vector can then be used as the input of a discriminative classifier. We compare the performance of the proposed approach with that of a generative baseline on a challenging real-world dataset of customer letters. When the kernel used by the classifier is linear, the performance improvement is marginal but the proposed system is approximately 15 times faster than the baseline. If we use a non-linear kernel devised for this task, we obtain a 15% relative reduction of the error but the detector is approximately 15 times slower. © 2009 IEEE.

关键词： pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Handwritten word-image retrieval with synthesized typed queries

Handwritten word-image retrieval with synthesized typed quer...

引用

ICDAR2009 - 10th International Conference on Document analysis and Recognition

作者： Rodríguez-Serrano, José A. Perronnin, Florent Computer Vision Centre Universitat Autonoma de Barcelona Spain Textual and Visual Pattern Analysis Xerox Research Centre Europe France Computer Science Department Loughborough University United Kingdom

ISBN: (纸本)9780769537252

We propose a new method for handwritten word-spotting which does not require prior training or gathering examples for querying. More precisely, a model is trained "on the fly" with images rendered from the searched words in one or multiple computer fonts. To reduce the mismatch between the typed-text prototypes and the candidate handwritten images, we make use of: (i) local gradient histogram (LGH) features, which were shown to model word shapes robustly, and (ii) semi-continuous hidden Markov models (SC-HMM), in which the typed-text models are constrained to a "vocabulary" of handwritten shapes, thus learning a link between both types of data. Experiments show that the proposed method is effective in retrieving handwritten words, and the comparison to alternative methods reveals that the contribution of both the LGH features and the SC-HMM is crucial. To the best of the authors' knowledge, this is the first work to address this issue in a non-trivial manner. © 2009 IEEE.

关键词： Image retrieval

来源：评论

学校读者我要写书评

暂无评论

Modeling images as mixtures of reference images

Modeling images as mixtures of reference images

引用

Conference on Computer Vision and pattern Recognition (CVPR)

作者： Florent Perronnin Yan Liu Xerox Research Centre Europe (XRCE) Textual and Visual Pattern Analysis (TVPA) France

A state-of-the-art approach to measure the similarity of two images is to model each image by a continuous distribution, generally a Gaussian mixture model (GMM), and to compute a probabilistic similarity between the GMMs. One limitation of traditional measures such as the Kullback-Leibler (KL) divergence and the probability product kernel (PPK) is that they measure a global match of distributions. This paper introduces a novel image representation. We propose to approximate an image, modeled by a GMM, as a convex combination of K reference image GMMs, and then to describe the image as the K-dimensional vector of mixture weights. The computed weights encode a similarity that favors local matches (i.e. matches of individual Gaussians) and is therefore fundamentally different from the KL or PPK. Although the computation of the mixture weights is a convex optimization problem, its direct optimization is difficult. We propose two approximate optimization algorithms: the first one based on traditional sampling methods, the second one based on a variational bound approximation of the true objective function. We apply this novel representation to the image categorization problem and compare its performance to traditional kernel-based methods. We demonstrate on the PASCAL VOC 2007 dataset a consistent increase in classification accuracy.

关键词： Image representation Kernel Vocabulary pattern analysis europe Distributed computing Optimization methods Approximation algorithms Sampling methods Image classification

来源：评论

学校读者我要写书评

暂无评论

Handwritten Word Image Retrieval with Synthesized Typed Queries

Handwritten Word Image Retrieval with Synthesized Typed Quer...

引用

International Conference on Document analysis and Recognition

作者： José A. Rodríguez-Serrano Florent Perronnin Computer Vision Centre Universitat Authnòma de Barcelona Spain Textual and Visual Pattern Analysis Xerox Research Centre Europe France

We propose a new method for handwritten word-spotting which does not require prior training or gathering examples for querying. More precisely, a model is trained ldquoon the flyrdquo with images rendered from the searched words in one or multiple computer fonts. To reduce the mismatch between the typed-text prototypes and the candidate handwritten images, we make use of: (i) local gradient histogram(LGH) features, which were shown to model word shapes robustly, and (ii) semi-continuous hidden Markov models(SC-HMM), in which the typed-text models are constrained to a ldquovocabularyrdquo of handwritten shapes, thus learning a link between both types of data. Experiments show that the proposed method is effective in retrieving handwritten words, and the comparison to alternative methods reveals that the contribution of both the LGH features and the SCHMM is crucial. To the best of the authorspsila knowledge, this is the first work to address this issue in a non-trivial manner.

关键词： Image retrieval Hidden Markov models Prototypes Shape Robustness pattern analysis Handwriting recognition Rendering (computer graphics) Writing Text analysis

来源：评论

学校读者我要写书评

暂无评论

Fisher Kernels for Handwritten Word-spotting

Fisher Kernels for Handwritten Word-spotting

引用

International Conference on Document analysis and Recognition

作者： Florent Perronnin Jose A. Rodriguez-Serrano Textual and Visual Pattern Analysis Xerox Research Centre Europe France Computer Vision Centre Universitat Authnòma de Barcelona Spain

The Fisher kernel is a generic framework which combines the benefits of generative and discriminative approaches to pattern classification. In this contribution, we propose to apply this framework to handwritten word-spotting. Given a word image and a keyword generative model, the idea is to generate a vector which describes how the parameters of the keyword model should be modified to best fit the word *** vector can then be used as the input of a discriminative classifier. We compare the performance of the proposed approach with that of a generative baseline on a challenging real-world dataset of customer letters. When the kernel used by the classifier is linear, the performance improvement is marginal but the proposed system is approximately 15 times faster than the baseline. If we use a non-linear kernel devised for this task, we obtain a 15% relative reduction of the error but the detector is approximately 15 times slower.

关键词： Kernel Hidden Markov models pattern classification pattern analysis europe Text analysis Handwriting recognition pattern recognition Computer vision Vectors

来源：评论

学校读者我要写书评

暂无评论

A family of contextual measures of similarity between distributions with application to image retrieval

A family of contextual measures of similarity between distri...

引用

Conference on Computer Vision and pattern Recognition (CVPR)

作者： Florent Perronnin Yan Liu Jean-Michel Renders Textual and Visual Pattern Analysis (TVPA) Xerox Research Centre Europe France Laboratoire dInformatique en Image et Systmes dInformation (LIRIS) Ecole Centrale de Lyon Limited France

We introduce a novel family of contextual measures of similarity between distributions: the similarity between two distributions q and p is measured in the context of a third distribution u. In our framework any traditional measure of similarity / dissimilarity has its contextual counterpart. We show that for two important families of divergences (Bregman and Csisz'ar), the contextual similarity computation consists in solving a convex optimization problem. We focus on the case of multinomials and explain how to compute in practice the similarity for several well-known measures. These contextual measures are then applied to the image retrieval problem. In such a case, the context u is estimated from the neighbors of a query q. One of the main benefits of our approach lies in the fact that using different contexts, and especially contexts at multiple scales (i.e. broad and narrow contexts), provides different views on the same problem. Combining the different views can improve retrieval accuracy. We will show on two very different datasets (one of photographs, the other of document images) that the proposed measures have a relatively small positive impact on macro Average Precision (which measures purely ranking) and a large positive impact on micro Average Precision (which measures both ranking and consistency of the scores across multiple queries).

关键词： Image retrieval Q measurement Painting Information retrieval Cats Rendering (computer graphics) pattern analysis europe Humans Animals

来源：评论

学校读者我要写书评

暂无评论

引用

26th IEEE Conference on Computer Vision and pattern Recognition (CVPR 2008), vol.7

作者： Yan Liu Florent Perronnin Xerox Research Centre Europe (XRCE) Textual and Visual Pattern Analysis (TVPA)

We present a novel approach to compute the similarity between two unordered variable-sized vector sets. To solve this problem, several authors have proposed to model each vector set with a Gaussian mixture model (GMM) and to compute a probabilistic measure of similarity between the GMMs. The main contribution of this paper is to model each vector set with a GMM adapted from a common "universal" GMM using the maximum a posteriori (MAP) criterion. The advantages of this approach are twofold. MAP provides a more accurate estimate of the GMM parameters compared to standard maximum likelihood estimation (MLE) in the challenging case where the cardinality of the vector set is small. Moreover, there is a correspondence between the Gaussians of two GMMs adapted from a common distribution and one can take advantage of this fact to compute efficiently the probabilistic similarity. This work is applied to the image categorization problem: images are modeled as bags of low-level features and classification is performed using a kernel classifier based on the proposed similarity measure. Experimental results on the PASCAL VOC 2006 and VOC 2007 databases show the excellent performance of our approach.

关键词： Vocabulary Costs Maximum likelihood estimation Kernel pattern analysis Distributed computing Performance evaluation Histograms Feature extraction Sampling methods

来源：评论

学校读者我要写书评

暂无评论

Unsupervised writer style adaptation for handwritten word spotting

Unsupervised writer style adaptation for handwritten word sp...

引用

International Conference on pattern Recognition

作者： Jose A. Rodriguez Florent Perronnin Gemma Sanchez Josep Llados Xerox Research Centre Europe Textual and Visual Pattern Analysis (TVPA) France Computer Vision Center (CVC) Universitat Authnòma de Barcelona Spain

ISBN: (纸本)9781424421749

We propose a novel approach for writer adaptation in a word spotting task. The method exploits the fact that a semi-continuous hidden Markov model separates the word model parameters into (i) a shared codebook of shapes and (ii) a set of word-specific parameters. Our main contribution is to derive writer-specific word models by statistically adapting an initial universal codebook to each document. This process is unsupervised and does not even require the appearance of the keyword(s) in the searched document. Experimental results show an increase in performance when this adaptation technique is applied. To the best knowledge of the authors, this is the first work dealing with adaptation for word spotting.

关键词： Hidden Markov models Character recognition Handwriting recognition Shape Testing pattern analysis europe Computer vision pattern classification Degradation

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共2页 << < 1 2 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：