Learning interpretable binary codes via semantic alignment for customized image retrieval
文献类型：期刊论文
| 作者 | Qiao, Shishi1,2,3; Wang, Ruiping2,3; Chen, Xilin2,3 |
| 刊名 | PATTERN RECOGNITION
 |
| 出版日期 | 2026-04-01 |
| 卷号 | 172页码:14 |
| 关键词 | Image retrieval Hashing Interpretability Semantic alignment |
| ISSN号 | 0031-3203 |
| DOI | 10.1016/j.patcog.2025.112380 |
| 英文摘要 | The single modality hashing (SMH) has achieved impressive performance on image retrieval task in recent years. The only fly in the ointment is that most of the methods mainly measure the image similarity based on the highlevel class labels. The retrieval needs in the real world are diverse in form of different subsets of the semantics (not only the category labels) presented in the query image. However, existing SMH methods fail to account for such customized image retrieval task that allows users to select visual semantics or their combinations present in the query and retrieve similar images based on such selected semantic descriptions. To address such practical issues, we propose a deep hashing to learn Interpretable Binary Codes (IBC), endowing the hashing bits with semantic interpretability rather than purely entangling the class information in the whole codes, i.e., aligning the criteria of binary space partition of each bit with a particular visual semantic concept. Specifically, binary encoding is a highly non-linear operation of dimension reduction, the semantic and spatial information of which has respectively been abstract and lost heavily. In light of the rich semantic interpretability and binary concept detection ability of convolutional filters, we innovatively transfer the semantic knowledge from filters to hashing bits by align the distributions of the binary codes and filter activations that capture the presence/absence of visual patterns in images. To further improve the semantics of filters/bits, the shared and learnable classification rules are introduced and optimized to disentangle the sparse composition between the category label and encoded semantics in filters/bits. With high interpretability, we can selectively combine bits corresponding to the target semantics during retrieval, thereby enabling flexible and customized similarity searches. Extensive experiments on several large-scale datasets covering general objects and scenes, single and multiple label scenarios, demonstrate the interpretability and functionalities of learned binary codes for the customized image retrieval tasks. |
| 资助项目 | National Key R&D Program of China[2021ZD0111901] ; Natural Science Foundation of China[U21B2025] ; Natural Science Foundation of China[U19B2036] ; Natural Science Foundation of China[61922080] ; Natural Science Foundation of China[62206260] ; Natural Science Foundation of Shandong Province[ZR2024QF077] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001566676700011 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/41724]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Wang, Ruiping |
| 作者单位 | 1.Ocean Univ China, Fac Informat Sci & Engn, Qingdao 266100, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Qiao, Shishi,Wang, Ruiping,Chen, Xilin. Learning interpretable binary codes via semantic alignment for customized image retrieval[J]. PATTERN RECOGNITION,2026,172:14. |
| APA | Qiao, Shishi,Wang, Ruiping,&Chen, Xilin.(2026).Learning interpretable binary codes via semantic alignment for customized image retrieval.PATTERN RECOGNITION,172,14. |
| MLA | Qiao, Shishi,et al."Learning interpretable binary codes via semantic alignment for customized image retrieval".PATTERN RECOGNITION 172(2026):14. |
入库方式： OAI收割
来源：计算技术研究所
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