Institute of Computing Technology, Chinese Academy IR
Unsupervised Cross-Modal Hashing via Semantic Text Mining | |
Tu, Rong-Cheng1; Mao, Xian-Ling1; Lin, Qinghong2; Ji, Wenjin1; Qin, Weize3; Wei, Wei4; Huang, Heyan1 | |
2023 | |
发表期刊 | IEEE TRANSACTIONS ON MULTIMEDIA |
ISSN | 1520-9210 |
卷号 | 25页码:8946-8957 |
摘要 | Cross-modal hashing has been widely used in multimedia retrieval tasks due to its fast retrieval speed and low storage cost. Recently, many deep unsupervised cross-modal hashing methods have been proposed to deal the unlabeled datasets. These methods usually construct an instance similarity matrix by fusing the image and text modality-specific similarity matrices as the guiding information to train the hashing networks. However, most of them directly use cosine similarities between the bag-of-words (BoW) vectors of text datapoints to define the text modality-specific similarity matrix, which fails to mine the semantic similarity information contained in the text modal datapoints and leads to the poor quality of the instance similarity matrix. To tackle the aforementioned problem, in this paper, we propose a novel Unsupervised Cross-modal Hashing via Semantic Text Mining, called UCHSTM. Specifically, UCHSTM first mines the correlations between the words of text datapoints. Then, UCHSTM constructs the text modality-specific similarity matrix for the training instances based on the mined correlations between their words. Next, UCHSTM fuses the image and text modality-specific similarity matrices as the final instance similarity matrix to guide the training of hashing model. Furthermore, during the process of training the hashing networks, a novel self-redefined-similarity loss is proposed to further correct some wrong defined similarities in the constructed instance similarity matrix, thereby further enhancing the retrieval performance. Extensive experiments on two widely used datasets show that the proposed UCHSTM outperforms state-of-the-art baselines on cross-modal retrieval tasks. |
关键词 | Cross-modal retrieval deep supervised hashing semantic text mining self-redefined-similarity loss |
DOI | 10.1109/TMM.2023.3243608 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Ramp;D Plan |
WOS研究方向 | Computer Science ; Telecommunications |
WOS类目 | Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications |
WOS记录号 | WOS:001133278300011 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.204/handle/2XEOYT63/38414 |
专题 | 中国科学院计算技术研究所期刊论文_英文 |
通讯作者 | Mao, Xian-Ling |
作者单位 | 1.Beijing Inst Technol, Dept Comp Sci & Technol, Beijing 100081, Peoples R China 2.Shenzhen Univ, Sch Comp Sci & Software Engn, Shenzhen 518052, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 4.Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan 430074, Peoples R China |
推荐引用方式 GB/T 7714 | Tu, Rong-Cheng,Mao, Xian-Ling,Lin, Qinghong,et al. Unsupervised Cross-Modal Hashing via Semantic Text Mining[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2023,25:8946-8957. |
APA | Tu, Rong-Cheng.,Mao, Xian-Ling.,Lin, Qinghong.,Ji, Wenjin.,Qin, Weize.,...&Huang, Heyan.(2023).Unsupervised Cross-Modal Hashing via Semantic Text Mining.IEEE TRANSACTIONS ON MULTIMEDIA,25,8946-8957. |
MLA | Tu, Rong-Cheng,et al."Unsupervised Cross-Modal Hashing via Semantic Text Mining".IEEE TRANSACTIONS ON MULTIMEDIA 25(2023):8946-8957. |
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