检索结果-内蒙古大学图书馆

International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Mengyi Liu Xin Liu Yan Li Xilin Chen Alexander G. Hauptmann Shiguang Shan Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Beijing China School of Computer Science Carnegie Mellon University USA

Cultural events are kinds of typical events closely related to history and nationality, which play an important role in cultural heritage through generations. However, automatically recognizing cultural events still remains a great challenge since it depends on understanding of complex image contents such as people, objects, and scene context. Therefore, it is intuitive to associate this task with other high-level vision problems, e.g., object detection, recognition, and scene understanding. In this paper, we address this problem by combining both ideas of object / scene contents mining and strong image representation via CNN into a whole framework. Specifically, for object / scene contents mining, we employ selective search to extract a batch of bottom-up region proposals, which are served as key object / scene candidates in each event image, while for representation via CNN, we investigate two state-of-the-art deep architectures, VGGNet and GoogLeNet, and adapt them to our task by performing domain-specific (i.e., event) fine-tuning on both global image and hierarchical region proposals. These two models can complementarily exploit feature hierarchies spatially, which simultaneously capture the global context and local evidences within the image. In our final submission for ChaLearn LAP Challenge ICCV 2015, nine kinds of features extracted from five different deep models were exploited and followed with two kinds of classifiers for decision level fusion. Our method achieves the best performance of mAP=0.854 among all the participants in the track of cultural event recognition.

关键词： Cultural differences Feature extraction Proposals Image recognition Visualization Search problems Neural networks

来源：评论

学校读者我要写书评

暂无评论

A new logistic map based chaotic biogeography-based optimization approach for cluster analysis

A new logistic map based chaotic biogeography-based optimiza...

引用

IEEE International Conference on Progress in Informatics and computing (PIC)

作者： Shui-Hua Wang Hui-Min Lu Yu-Jie Li Yang Wang Zhi-Min Chen Yu-Min Wei Wen-Juan Jia Fang-Yuan Liu Yu-Dong Zhang Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Policy Academy Changsha Hunan China School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China Department of Electrical Engineering CUNY New York NY USA Department of Mechanical and Control Engineering Kyushu Institute of Technology Fukuoka Prefecture Japan School of Information Engineering Yangzhou University Yangzhou Jiangsu China School of Physics and Electronic Engineering Taizhou University Taizhou Zhejiang China School of Electronic Information Shanghai Dianji University Shanghai China School of Energy and Mechanical Engineering Nanjing Normal University Nanjing Jiangsu China Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University Quanzhou Normal University Quanzhou Fujian China

ISBN: (纸本)9781509034857

Cluster analysis is important in scientific and industrial fields. In this study, we proposed a novel chaotic biogeography-based optimization (CBBO) method, and applied it in centroid-based clustering methods. The results over three types of simulation data showed that this proposed CBBO method gave better performance than chaotic particle swarm optimization, genetic algorithm, firefly algorithm, and quantum-behaved particle swarm optimization. In all, our CBBO method is effective in centroid-based clustering.

关键词： Genetic algorithms Optimization Clustering algorithms Particle swarm optimization Algorithm design and analysis Logistics Mathematical model

来源：评论

学校读者我要写书评

暂无评论

Motivation Learning in Mind Model CAM

引用

International Journal of Intelligence Science 2015年第2期5卷 63-71页

作者： Zhongzhi Shi Gang Ma Xi Yang Chengxiang Lu Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Information Science and Engineering Qufu Normal University Rizhao China

Motivation learning aims to create abstract motivations and related goals. It is one of the high-level cognitive functions in Consciousness And Memory model (CAM). This paper proposes a new motivation learning algorithm which allows an agent to create motivations or goals based on introspective process. The simulation of cyborg rat maze search shows that the motivation learning algorithm can adapt agents’ behavior in response to dynamic environment.

关键词： Motivation Learning Motivation processing Consciousness and Memory Model Cyborg Rat

来源：评论

学校读者我要写书评

暂无评论

QACtools: A Quality Assessment and Quality Control Tool for Next-Generation Sequencing Data

QACtools: A Quality Assessment and Quality Control Tool for ...

引用

2015年中国智能自动化学术会议

作者： Dandan Song Ning Li Lejian Liao Beijing Lab of Intelligent Information Technology School of Computer Science and Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology

In recent years, next-generation sequencing technologies have been widely used, and are rapidly changing the landscape of genetics with unimaginable speed. However, the preliminary quality control regulations including trimming adaptor sequences, quality statistics, and depth and coverage statistics need to be applied to raw reads before further analysis. QACtools implements various utilities for processing raw reads in the FASTQ format and alignments in the BAM *** analyzes some relevant properties of next-generation sequencing data such as average read length, base quality scores, and so on. Additionally, to our knowledge,none of the other tools support depth and coverage statistic that have been provided in our tool for analysis of large DNA sequencing data. It is crucial for overcoming errors in base calling and assembly. We suppose tool is useful for the quality control of NGS data to perform further analysis. This software is an open source application freely available at https://***/projects/qactools/.

关键词： NGS data analysis Quality control FASTQ format BAM format

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 (ChinaSIP)

作者： Xiaohui Fu Li Su Lei Qin University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Key Lab on Big Data Mining and Knowledge Management Beijing China Key Lab of Intelligent Information Processing Institute of Computing Technology Beijing China

ISBN: (纸本)9781479919499

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： Decision support systems Feature extraction Semantics Training Videos Indexes Training data

来源：评论

学校读者我要写书评

暂无评论

A Study on Apparent Age Estimation

A Study on Apparent Age Estimation

引用

International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Yu Zhu Yan Li Guowang Mu Guodong Guo Lane Department of CSEE West Virginia University Morgantown WV USA Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

Age estimation from facial images is an important problem in computer vision and pattern recognition. Typically the goal is to predict the chronological age of a person given his or her face picture. It is seldom to study a related problem, that is, how old does a person look like from?the face photo? It is called apparent age estimation. A key difference between apparent age estimation and the traditional age estimation is that the age labels are annotated by human assessors rather than the real chronological age. The challenge for apparent age estimation is that there are?not many face images available with annotated age labels. Further, the annotated age labels for each face photo may not be consistent among different assessors. We study the problem of apparent age estimation by addressing the issues from different aspects, such as how to utilize a large number of face images without apparent age labels to learn a face representation using the deep neural networks, how to tune the deep networks using a limited number of examples with apparent age labels, and how well the machine learning methods can perform to estimate apparent ages. The apparent age data is from the ChaLearn Looking At People (LAP) challenge 2015. Using the protocol and time frame given by the challenge competition, we have achieved an error of 0.294835 on the final evaluation, and our result has been ranked the 3rd place in this competition.

关键词： Estimation Face Databases Training Neural networks Data models Computer architecture

来源：评论

学校读者我要写书评

暂无评论

Corrections to “Cross-Modal Correlation Learning by Adaptive Hierarchical Semantic Aggregation” [Jun 16 1201-1216]

引用

IEEE Transactions on Multimedia 2016年第10期18卷 2127-2127页

作者： Yan Hua Shuhui Wang Siyuan Liu Anni Cai Qingming Huang School of Information Engineering Communication University of China Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Smeal College of Business Pennsylvania State University University Park PA USA Center of Cloud Computing Institute of Advanced Computing and Digital Engineering Shenzhen Institutes of Advanced Technology Shenzhen China Department of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Intellectual Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Presents corrections to the paper, "Cross-modal correlation learning by adaptive hierarchical semantic aggregation," (Hua, Y., et al) IEEE Trans. Multimedia, vol. 18, no. 6, pp. 1201-1216, Jun. 2016.

关键词： Semantics Learning (artificial intelligence) Ontologies Query processing

来源：评论

学校读者我要写书评

暂无评论

Fast assignment reduction in inconsistent incomplete decision systems

引用

Journal of Systems Engineering and Electronics 2014年第1期25卷 83-94页

作者： Min Li Shaobo Deng Shengzhong Feng Jianping Fan Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Nanchang Institute of Technology Graduate School of Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences

This paper focuses on fast algorithm for computing the assignment reduct in inconsistent incomplete decision systems. It is quite inconvenient to judge the assignment reduct directly ac-cording to its definition. We propose the judgment theorem for the assignment reduct in the inconsistent incomplete decision system, which greatly simplifies judging this type reduct. On such basis, we derive a novel attribute significance measure and construct the fast assignment reduction algorithm （F-ARA）, intended for com-puting the assignment reduct in inconsistent incomplete decision systems. Final y, we make a comparison between F-ARA and the discernibility matrix-based method by experiments on 13 Univer-sity of California at Irvine （UCI） datasets, and the experimental results prove that F-ARA is efficient and feasible.

关键词： assignment reduction upper approximation reduction,inconsistent incomplete decision system rough set.

来源：评论

学校读者我要写书评

暂无评论

OBJECT SEGMENTATION WITH DEEP REGRESSION

OBJECT SEGMENTATION WITH DEEP REGRESSION

引用

IEEE International Conference on Image processing

作者： Jianchao Li Dan Wang Canxiang Yan Shiguang Shan Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS University of Chinese Academy of Sciences Institute of Deep Learning Baidu Inc.

ISBN: (纸本)9781479983407

Object segmentation has constantly received much attention due to its fundamental role in scene understanding. Traditional methods formulate it as a structured prediction problem, represented by graphical models (GMs). However, most GMs have difficulties in balancing the effectiveness of context modeling and efficiency of model inference. In this paper, we model the contexts implicitly using the deep convolutional neural network (DCNN). Specifically, we reformulate object segmentation as a regression problem and train a deep network end-to-end to learn the nonlinear mapping from the image to the object mask. The large receptive field of the network incorporates wide contexts to update the network parameters, giving an implicit context model. Moreover, the deep architecture is favorable for modeling nonlinearity. The inference of our method is quite efficient, involving only a simple feed-forward pass. Extensive experiments on public datasets demonstrate the advantages of our method.

关键词： Object segmentation Context modeling Deep convolutional neural networks Regression Object segmentation Context modeling Regression (Psychology) network parameters Regression Regression

来源：评论

学校读者我要写书评

暂无评论

Author Correction: The challenge of mapping the human connectome based on diffusion tractography

引用

Nature communications 2019年第1期10卷 5059页

作者： Klaus H Maier-Hein Peter F Neher Jean-Christophe Houde Marc-Alexandre Côté Eleftherios Garyfallidis Jidan Zhong Maxime Chamberland Fang-Cheng Yeh Ying-Chia Lin Qing Ji Wilburn E Reddick John O Glass David Qixiang Chen Yuanjing Feng Chengfeng Gao Ye Wu Jieyan Ma Renjie He Qiang Li Carl-Fredrik Westin Samuel Deslauriers-Gauthier J Omar Ocegueda González Michael Paquette Samuel St-Jean Gabriel Girard François Rheault Jasmeen Sidhu Chantal M W Tax Fenghua Guo Hamed Y Mesri Szabolcs Dávid Martijn Froeling Anneriet M Heemskerk Alexander Leemans Arnaud Boré Basile Pinsard Christophe Bedetti Matthieu Desrosiers Simona Brambati Julien Doyon Alessia Sarica Roberta Vasta Antonio Cerasa Aldo Quattrone Jason Yeatman Ali R Khan Wes Hodges Simon Alexander David Romascano Muhamed Barakovic Anna Auría Oscar Esteban Alia Lemkaddem Jean-Philippe Thiran H Ertan Cetingul Benjamin L Odry Boris Mailhe Mariappan S Nadar Fabrizio Pizzagalli Gautam Prasad Julio E Villalon-Reina Justin Galvis Paul M Thompson Francisco De Santiago Requejo Pedro Luque Laguna Luis Miguel Lacerda Rachel Barrett Flavio Dell'Acqua Marco Catani Laurent Petit Emmanuel Caruyer Alessandro Daducci Tim B Dyrby Tim Holland-Letz Claus C Hilgetag Bram Stieltjes Maxime Descoteaux Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. k.maier-hein@dkfz.de. Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. Sherbrooke Connectivity Imaging Lab (SCIL) Université de Sherbrooke Sherbrooke QC J1K 0A5 QC Canada. Department of Intelligent Systems Engineering School of Informatics and Computing Indiana University Bloomington IN 47408 USA. Krembil Research Institute University Health Network Toronto Canada M5G 2C4. Department of Neurological Surgery University of Pittsburgh School of Medicine Pittsburgh PA 15213 USA. IMT-Institute for Advanced Studies Lucca 55100 Italy. Department of Diagnostic Imaging St. Jude Children's Research Hospital Memphis TN 38105 USA. University of Toronto Institute of Medical Science Toronto Canada M5S 1A8. Institute of Information Processing and Automation Zhejiang University of Technology Hangzhou 310023 Zhejiang China. United Imaging Healthcare Co Shanghai 201807 China. Shanghai Advanced Research Institute Shanghai 201210 China. Laboratory of Mathematics in Imaging Harvard Medical School Boston MA 02215 USA. Center for Research in Mathematics Guanajuato 36023 Mexico. PROVIDI Lab Image Sciences Institute University Medical Center Utrecht Utrecht 3508 The Netherlands. Cardiff University Brain Research Imaging Centre School of Psychology Cardiff University Maindy Road Cardiff CF24 4HQ UK. Department of Radiology University Medical Center Utrecht Utrecht 3508 The Netherlands. Centre de recherche institut universitaire de geriatrie de Montreal (CRIUGM) Université de Montréal Montreal QC Canada H3W 1W5. Sorbonne Universités UPMC Univ Paris 06 CNRS INSERM Laboratoire d'Imagerie Biomédicale (LIB) 75013 Paris France. Center for Advanced Research in Sleep Medicine Hôpital du Sacré-Coeur de Montréal Montreal Canada H4J 1C5. Neuroimaging Unit Institute of Bioimaging and Molecular Physiology (IBFM) Nat

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词：

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：