Abstract
The purpose of this paper is to solve some issues in cross-media retrieval of collaboratively plotted multimedia data. Under the premise of global dynamic heterogeneous spatial information collaborative plotting and spatial knowledge service technical support, in this thesis, the characteristics of the retrieved data is seen as a prerequisite, and combined the advantages of collaborative plots generated multimedia data, using distributed deep learning technology and container cloud in the technology, the features of the extracted data are formed into a database of data feature vectors, and the UFM-LF model is proposed to modify and filter the features of the plotted data so as to improve the accuracy of data retrieval and thus provide the user with knowledge services. The experimental data shows that under the same conditions, the computing power of the container-based cloud platform is about 2.6 times as powerful as that of the stand-alone combination, which provides a powerful computing capability for the training task; Besides, combining the deep learning model with the UFM-LF model improves the accuracy of the retrieved data significantly. The platform can provide powerful computing capabilities and improve the accuracy of cross-media data retrieval. It has good scalability and can be applied to data retrieval in other fields.
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Acknowledgements
This research work was supported by the National Natural Science Foundation of China (Grant No. 61762031), Guangxi Key Research and Development Plan (Nos. 2017AB51024, 2018AB8126006), Guangxi Key Laboratory Fund of Embedded Technology and Intelligent System.
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Xie, X., Zheng, Q., Li, X., Cheng, X., Guo, Z. (2019). Research on Cross-Media Retrieval of Collaborative Plotted Multimedia Data Based on Container-Based Cloud Platform and Deep Learning. In: Sun, Y., Lu, T., Xie, X., Gao, L., Fan, H. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2018. Communications in Computer and Information Science, vol 917. Springer, Singapore. https://doi.org/10.1007/978-981-13-3044-5_30
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