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First, based on the multidescriptor hierarchical feature, the middle-level local feature extraction of the water body is achieved, and, combined with the convolutional neural network, the high-order global features of the water body are extracted. Then, the image features are hierarchically normalized, and multimodal RBM self-encoding is used for fusion to reduce the quantization error of each layer feature in the encoding process. Finally, the generative model of the Multimodal Model is used to expand the data and filter the multilayered features after fusion. In addition, the semantic information of a water body is further discovered by using the encoder and decoder of the discriminant model and is classified by employing SoftMax. The results show that compared with the traditional water body identification methods, the proposed method improves the recognition accuracy and image description ability.<\/jats:p>","DOI":"10.1142\/s1469026819500159","type":"journal-article","created":{"date-parts":[[2019,7,12]],"date-time":"2019-07-12T08:44:31Z","timestamp":1562921071000},"source":"Crossref","is-referenced-by-count":1,"title":["Water Body Semantic Information Description and Recognition Based on Multimodal Models"],"prefix":"10.1142","volume":"18","author":[{"given":"Yalong","family":"Lv","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, No. 666, Shengli Road, Say Bagh District, Urumqi, Xinjiang 830046, P. R. 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