LPGOH: Label-prototype guided online hashing for efficient cross-modal retrieval

IF 7.6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Shu-Juan Peng , Xueting Jiang , Xin Liu , Ji-Xiang Du , Jianjia Cao
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引用次数: 0

Abstract

Cross-modal hashing has recently received widespread attention due to its fast query speed, and existing batch-based methods are generally inefficient in an online scenario, i.e., multi-modal data points appear in a streaming manner. Although some online cross-modal hashing methods have been explored, they often neglect the semantic interdependency among the label categories and potentially suffer from the limited semantic preservations between newly coming data and existing data. To alleviate this concern, this paper proposes an efficient label-prototype guided online hashing (LPGOH) for cross-modal retrieval, which can incrementally learn the discriminative hash codes of streaming data while adaptively optimizing the hash function in a streaming manner. To be specific, the proposed framework first innovates a group of label-prototype codes to exploit the semantic interdependency between the label categories, and then combine the semantic similarity regularization to jointly learn the semantic-preserving hash codes. Meanwhile, ε-dragging operation is seamlessly utilized to provide provable large semantic margins, which can further promote the discrimination power of the learnt hash code and speed up the learning process. Besides, an online discrete optimization algorithm is efficiently designed to parse the semantic interdependency between the label categories, learn the compact hash codes for the current arriving data, and optimize the hash functions adaptively. Accordingly, the hash codes of streaming data are discriminatively learned to benefit various online cross-modal retrieval tasks. Extensive experiments evaluated on benchmark datasets verify the advantages of the proposed LPGOH framework, by achieving the competitive and mostly improved retrieval performance over the state-of-the-arts.
LPGOH:标签原型引导在线哈希高效跨模态检索
跨模态哈希由于其快速的查询速度受到了广泛的关注,而现有的基于批处理的方法在在线场景下通常效率低下,即多模态数据点以流的方式出现。尽管已经探索了一些在线跨模态哈希方法,但它们往往忽略了标签类别之间的语义相互依赖性,并且可能存在新数据与现有数据之间语义保留有限的问题。为了缓解这一问题,本文提出了一种高效的标签原型引导在线哈希(LPGOH)跨模态检索方法,该方法可以增量学习流数据的判别哈希码,同时以流方式自适应优化哈希函数。该框架首先创新一组标签原型码,利用标签类别之间的语义相互依赖性,然后结合语义相似度正则化,共同学习保持语义的哈希码。同时,无缝利用ε-拖拽运算提供可证明的大语义余量,进一步提高了学习到的哈希码的识别能力,加快了学习过程。此外,设计了一种在线离散优化算法,有效地解析标签类别之间的语义依存关系,学习当前到达数据的紧凑哈希码,并自适应优化哈希函数。因此,流式数据的哈希码被判别学习,以有利于各种在线跨模态检索任务。在基准数据集上进行的大量实验验证了所提出的LPGOH框架的优势,通过实现与最先进的检索性能相比具有竞争力和很大程度上的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
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