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- W4214697488 abstract "In the process of cross-media retrieval, the original features of semantic information in images and texts may be lost, which will reduce the accuracy of cross-media retrieval. In order to solve the problem, we optimize the traditional common semantic subspace cross-media retrieval model to enhance semantic information by raising the distinction between the original features of the image and the text. First of all, using the Quadratic Discriminant Analysis (QDA) classifies the original data features of images and texts, mapping the images and texts into two different semantic enhancement subspaces respectively, and improving the discrimination between data. Next different mapping models are learned according to different retrieval tasks. Then the image and text feature matrix and semantic matrix are mapped into a common semantic subspace by using the mapping model. Eventually the similarity between image and text is calculated through Euclidean distance. We used the above method to conduct experiments on two datasets, including Wikipedia and Pascal Sentence. The experimental results show that the proposed method achieves better performance." @default.
- W4214697488 created "2022-03-02" @default.
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- W4214697488 date "2021-12-22" @default.
- W4214697488 modified "2023-10-14" @default.
- W4214697488 title "Semantic Enhanced Subspace Cross-Media Retrieval Based on Quadratic Discriminant Analysis" @default.
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- W4214697488 doi "https://doi.org/10.1145/3508546.3508631" @default.
- W4214697488 hasPublicationYear "2021" @default.
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