Nonlinear Robust Discrete Hashing for Cross-Modal Retrieval

Zhan Yang, J. Long, Lei Zhu, Wenti Huang
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引用次数: 14

Abstract

Hashing techniques have recently been successfully applied to solve similarity search problems in the information retrieval field because of their significantly reduced storage and high-speed search capabilities. However, the hash codes learned from most recent cross-modal hashing methods lack the ability to comprehensively preserve adequate information, resulting in a less than desirable performance. To solve this limitation, we propose a novel method termed Nonlinear Robust Discrete Hashing (NRDH), for cross-modal retrieval. The main idea behind NRDH is motivated by the success of neural networks, i.e., nonlinear descriptors, in the field of representation learning, and the use of nonlinear descriptors instead of simple linear transformations is more in line with the complex relationships that exist between common latent representation and heterogeneous multimedia data in the real world. In NRDH, we first learn a common latent representation through nonlinear descriptors to encode complementary and consistent information from the features of the heterogeneous multimedia data. Moreover, an asymmetric learning scheme is proposed to correlate the learned hash codes with the common latent representation. Empirically, we demonstrate that NRDH is able to successfully generate a comprehensive common latent representation that significantly improves the quality of the learned hash codes. Then, NRDH adopts a linear learning strategy to fast learn the hash function with the learned hash codes. Extensive experiments performed on two benchmark datasets highlight the superiority of NRDH over several state-of-the-art methods.
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非线性鲁棒离散散列跨模态检索
近年来,哈希技术因其显著降低存储容量和高速搜索能力而被成功地应用于解决信息检索领域的相似性搜索问题。然而,从最近的跨模态哈希方法中学习到的哈希码缺乏全面保存足够信息的能力,导致性能不理想。为了解决这一限制,我们提出了一种新的方法,称为非线性鲁棒离散哈希(NRDH),用于跨模态检索。NRDH背后的主要思想源于神经网络(即非线性描述符)在表征学习领域的成功,使用非线性描述符代替简单的线性变换更符合现实世界中常见潜在表征与异构多媒体数据之间存在的复杂关系。在NRDH中,我们首先通过非线性描述符学习一个共同的潜在表示,从异构多媒体数据的特征中编码互补和一致的信息。此外,提出了一种非对称学习方案,将学习到的哈希码与公共潜在表示相关联。经验上,我们证明了NRDH能够成功地生成一个全面的共同潜在表示,显著提高了学习到的哈希码的质量。然后,NRDH采用线性学习策略,利用学习到的哈希码快速学习哈希函数。在两个基准数据集上进行的大量实验突出了NRDH优于几种最先进的方法。
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