TY - JOUR
T1 - A Latent Feature-Based Multimodality Fusion Method for Theme Classification on Web Map Service
AU - Yang, Zelong
AU - Gui, Zhipeng
AU - Wu, Huayi
AU - Li, Wenwen
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China under Grant 41930107 and Grant 41971349.
Publisher Copyright:
© 2013 IEEE.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Cartography
KW - machine learning
KW - multimodality fusion
KW - theme classification
KW - web map service
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U2 - 10.1109/ACCESS.2019.2954851
DO - 10.1109/ACCESS.2019.2954851
M3 - Article
AN - SCOPUS:85079651595
SN - 2169-3536
VL - 8
SP - 25299
EP - 25309
JO - IEEE Access
JF - IEEE Access
M1 - 8908799
ER -