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- Title
在线图正则化非负矩阵分解跨模态哈希.
- Authors
罗雪梅; 郑海红; 安亚强; 王笛
- Abstract
Because of low storage cost and fast query speed, cross-modal hash is an effective cross-media retrieval method, which has been widely studied in recent years. However, most of the existing cross-modal hashing methods are based on the batch learning model, which does not effectively handle large datasets, consumes a lot of memory, and is inefficient in training stream data. Online learning can be used for cross-modal hashing to solve the above problems. However, most online cross-modal hashing methods mainly focus on mapping the data of different modes into a common low- dimensional space, so as to eliminate the heterogeneity between different modes and achieve cross-modal retrieval. However, the correlation between the data in the same mode is not fully exploited, and the final hashing code retrieval accuracy is not high. In this paper, online graph regularized non-negative matrix factorization cross-modal hashing (OGNMFH) retrieval method is proposed. By making full use of the local manifold structure information in the data mode and the category label information of the data, the inter-modal similarity and intra-modal similarity of the data are kept, and higher discriminant hash codes are obtained. A large number of experimental results on three classical datasets demonstrate that the OGNMFH method can improve retrieval accuracy in online hash learning.
- Subjects
MATRIX decomposition; ONLINE education; NONNEGATIVE matrices; DATA mapping; PROBLEM solving; VIDEO coding
- Publication
Journal of Frontiers of Computer Science & Technology, 2023, Vol 17, Issue 3, p678
- ISSN
1673-9418
- Publication type
Article
- DOI
10.3778/j.issn.1673-9418.2105039