...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Quality Control, Transactions >A Latent Feature-Based Multimodality Fusion Method for Theme Classification on Web Map Service
【24h】

A Latent Feature-Based Multimodality Fusion Method for Theme Classification on Web Map Service

机译：基于潜在特征的多模式融合方法，用于Web地图服务上的主题分类

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Massive maps have been shared as Web Map Service (WMS) from various providers, which could be used to facilitate people's daily lives and support space analysis and management. The theme classification of maps could help users efficiently find maps and support theme-related applications. Traditionally, metadata is usually used in analyzing maps content, few papers use maps, especially legends. In fact, people usually considers metadata, maps and legends together to understand what maps tell, however, no study has tried to exploit how to combine them. This paper proposes a method to fuse them with the purpose of classifying map themes, named latent feature based multimodality fusion for theme classification (LFMF-TC). Firstly, a multimodal dataset is created that supports the supervised classification on map themes. Secondly, textual and visual features are designed for metadata, maps, and legends using some advanced techniques. Thirdly, a latent feature based fusion method is proposed to fuse the multimodal features on the feature level. Finally, a neural network classifier is implemented using supervised learning on the multimodal dataset. In addition, a web-based collaboration platform is developed to facilitate users in labeling multimodal samples through an interactive Graphical User Interface (GUI). Extensive experiments are designed and implemented, whose results prove that LFMF-TC could significantly improve the classification accuracy. In theory, the LFMF-TC could be used for other applications with few modifications.
机译：来自各种提供商的MACLIVE地图已作为Web地图服务（WMS），可用于促进人们的日常生活和支持空间分析和管理。地图的主题分类可以帮助用户有效地查找映射和支持主题相关的应用程序。传统上，元数据通常用于分析地图内容，少数论文使用地图，尤其是传说。事实上，人们通常认为元数据，地图和传说在一起，了解哪些地图，但是，没有研究试图利用如何将它们结合起来。本文提出了一种融合它们的方法，目的是对地图主题进行分类，是基于潜在的特征基于的多模融合，用于主题分类（LFMF-TC）。首先，创建了一个多模式数据集，其支持地图主题上的监督分类。其次，文本和视觉功能专为使用某些高级技术而设计的元数据，地图和图例。第三，提出了一种基于潜在的融合方法来融合特征级别的多模峰特征。最后，在多模式数据集上使用监督学习实现神经网络分类器。此外，开发了一种基于Web的协作平台，以便通过交互式图形用户界面（GUI）来促进用户标记多模式样本。设计并实施了广泛的实验，其结果证明LFMF-TC可以显着提高分类准确性。理论上，LFMF-TC可用于其他应用少数修改。

著录项

  • 来源
    《Quality Control, Transactions》 |2020年第2020期|25299-25309|共11页
  • 作者

    Yang Zelong; Gui Zhipeng; Wu Huayi; Li Wenwen;

  • 作者单位

    Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Wuhan 430079 Peoples R China;

    Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Wuhan 430079 Peoples R China|Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China|Wuhan Univ Collaborat Innovat Ctr Geospatial Technol Wuhan 430079 Peoples R China;

    Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Wuhan 430079 Peoples R China|Wuhan Univ Collaborat Innovat Ctr Geospatial Technol Wuhan 430079 Peoples R China;

    Arizona State Univ Sch Geog Sci & Urban Planning Tempe AZ 85287 USA;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Cartography; machine learning; multimodality fusion; theme classification; web map service;

    机译：制图;机器学习;多模融合;主题分类;网站地图服务;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号