部分跨模态哈希的集体亲和学习。

IF 10.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jun Guo, Wenwu Zhu
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引用次数: 0

摘要

在过去的十年中,人们开发了各种用于跨模态检索的无监督哈希方法。然而,在现实世界的应用中,每种模态的数据都可能存在样本缺失的不完整情况。现有的大多数方法都假定每个对象都同时出现在两种模态中,因此它们可能无法很好地处理部分多模态数据。为了解决这个问题,我们提出了一种新颖的集体亲和学习方法(CALM),它能集体自适应地学习锚图,以便在部分多模态数据上生成二进制代码。在 CALM 中,我们首先集体构建特定模态的双方形图,然后推导出一个概率模型,为每种模态找出完整的数据到锚点的亲和力。理论分析表明,该模型能够恢复缺失的邻接信息。此外,还提出了一种稳健模型,通过自适应学习统一的锚图来融合这些特定模态的亲和力。然后,从学习到的锚图中获得的邻接信息作为反馈,指导之前的亲和性重建过程。为了解决所提出的优化问题,我们进一步开发了一种具有线性时间复杂性和快速收敛性的有效算法。最后,我们对融合后的亲和力进行了锚图散列(AGH),以实现跨模态检索。在基准数据集上的实验结果表明,我们提出的 CALM 始终优于现有方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Collective Affinity Learning for Partial Cross-Modal Hashing.

In the past decade, various unsupervised hashing methods have been developed for cross-modal retrieval. However, in real-world applications, it is often the incomplete case that every modality of data may suffer from some missing samples. Most existing works assume that every object appears in both modalities, hence they may not work well for partial multi-modal data. To address this problem, we propose a novel Collective Affinity Learning Method (CALM), which collectively and adaptively learns an anchor graph for generating binary codes on partial multi-modal data. In CALM, we first construct modality-specific bipartite graphs collectively, and derive a probabilistic model to figure out complete data-to-anchor affinities for each modality. Theoretical analysis reveals its ability to recover missing adjacency information. Moreover, a robust model is proposed to fuse these modality-specific affinities by adaptively learning a unified anchor graph. Then, the neighborhood information from the learned anchor graph acts as feedback, which guides the previous affinity reconstruction procedure. To solve the formulated optimization problem, we further develop an effective algorithm with linear time complexity and fast convergence. Last, Anchor Graph Hashing (AGH) is conducted on the fused affinities for cross-modal retrieval. Experimental results on benchmark datasets show that our proposed CALM consistently outperforms the existing methods.

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来源期刊
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing 工程技术-工程:电子与电气
CiteScore
20.90
自引率
6.60%
发文量
774
审稿时长
7.6 months
期刊介绍: The IEEE Transactions on Image Processing delves into groundbreaking theories, algorithms, and structures concerning the generation, acquisition, manipulation, transmission, scrutiny, and presentation of images, video, and multidimensional signals across diverse applications. Topics span mathematical, statistical, and perceptual aspects, encompassing modeling, representation, formation, coding, filtering, enhancement, restoration, rendering, halftoning, search, and analysis of images, video, and multidimensional signals. Pertinent applications range from image and video communications to electronic imaging, biomedical imaging, image and video systems, and remote sensing.
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