BITS: Bit-Extendable Incremental Hashing in Open Environments.
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| Title: | BITS: Bit-Extendable Incremental Hashing in Open Environments. |
|---|---|
| Authors: | Wang, Yongxin1 yxinwang@hotmail.com, Chen, Zhen-Duo2 chenzd.sdu@gmail.com, Luo, Xin2 luoxin.lxin@gmail.com, Xu, Xin-Shun2 xuxinshun@sdu.edu.cn |
| Source: | IEEE Transactions on Image Processing. 2025, Vol. 34, p6550-6563. 14p. |
| Subjects: | Hashing, Electronic file management, Image retrieval, Information retrieval, Machine learning |
| Abstract: | Hashing is an effective technique for large-scale image retrieval. However, traditional hashing models typically follow a closed-set assumption, which fails to satisfy the practicality of real-world tasks. In this paper, we explore a meaningful yet overlooked question: is there a hashing paradigm that not only supports rehearsal-free online incremental coding for single-pass data streams but also adapts to potentially expanding concept spaces in open environments? Instead of presetting fixed bit lengths, we suggest adjusting the bit length dynamically based on the number of encountered categories, meanwhile enabling bit extension of existing hash codes to match the adaptive code lengths without knowledge forgetting. Therefore, we propose a Bit-extendable IncremenTal haShing (BITS) method for image retrieval in open environments. Specifically, we identify a blurry incremental setup to better simulate realistic scenarios, revisiting the widely-used data-incremental and class-incremental settings. With this challenging setup, a three-phase framework is designed to efficiently perform incremental hashing, which jointly solves online continual coding and bit extension with adaptive code lengths. Through the well-designed hashing paradigm, BITS achieves comparable performance to offline hashing methods while significantly saving computational resources. Comprehensive experiments on six benchmarks demonstrate the superiority of our BITS in dynamic scenarios. The source code is available at https://github.com/yxinwang/BITS [ABSTRACT FROM AUTHOR] |
| Copyright of IEEE Transactions on Image Processing is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 191897274 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: BITS: Bit-Extendable Incremental Hashing in Open Environments. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Yongxin%22">Wang, Yongxin</searchLink><relatesTo>1</relatesTo><i> yxinwang@hotmail.com</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Zhen-Duo%22">Chen, Zhen-Duo</searchLink><relatesTo>2</relatesTo><i> chenzd.sdu@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Luo%2C+Xin%22">Luo, Xin</searchLink><relatesTo>2</relatesTo><i> luoxin.lxin@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Xu%2C+Xin-Shun%22">Xu, Xin-Shun</searchLink><relatesTo>2</relatesTo><i> xuxinshun@sdu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Image+Processing%22">IEEE Transactions on Image Processing</searchLink>. 2025, Vol. 34, p6550-6563. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Hashing%22">Hashing</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+file+management%22">Electronic file management</searchLink><br /><searchLink fieldCode="DE" term="%22Image+retrieval%22">Image retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Information+retrieval%22">Information retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Hashing is an effective technique for large-scale image retrieval. However, traditional hashing models typically follow a closed-set assumption, which fails to satisfy the practicality of real-world tasks. In this paper, we explore a meaningful yet overlooked question: is there a hashing paradigm that not only supports rehearsal-free online incremental coding for single-pass data streams but also adapts to potentially expanding concept spaces in open environments? Instead of presetting fixed bit lengths, we suggest adjusting the bit length dynamically based on the number of encountered categories, meanwhile enabling bit extension of existing hash codes to match the adaptive code lengths without knowledge forgetting. Therefore, we propose a Bit-extendable IncremenTal haShing (BITS) method for image retrieval in open environments. Specifically, we identify a blurry incremental setup to better simulate realistic scenarios, revisiting the widely-used data-incremental and class-incremental settings. With this challenging setup, a three-phase framework is designed to efficiently perform incremental hashing, which jointly solves online continual coding and bit extension with adaptive code lengths. Through the well-designed hashing paradigm, BITS achieves comparable performance to offline hashing methods while significantly saving computational resources. Comprehensive experiments on six benchmarks demonstrate the superiority of our BITS in dynamic scenarios. The source code is available at https://github.com/yxinwang/BITS [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Transactions on Image Processing is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/TIP.2025.3613924 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 6550 Subjects: – SubjectFull: Hashing Type: general – SubjectFull: Electronic file management Type: general – SubjectFull: Image retrieval Type: general – SubjectFull: Information retrieval Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: BITS: Bit-Extendable Incremental Hashing in Open Environments. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Yongxin – PersonEntity: Name: NameFull: Chen, Zhen-Duo – PersonEntity: Name: NameFull: Luo, Xin – PersonEntity: Name: NameFull: Xu, Xin-Shun IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 10577149 Numbering: – Type: volume Value: 34 Titles: – TitleFull: IEEE Transactions on Image Processing Type: main |
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