论文标题
无监督的深层模式光谱哈希
Unsupervised Deep Cross-modality Spectral Hashing
论文作者
论文摘要
本文提出了一个新颖的框架，即深度跨模式谱哈希（DCSH），以解决有效的跨模式检索的无监督学习问题。该框架是一种两步的哈希方法，将优化分解为（1）二进制优化和（2）哈希功能学习。在第一步中，我们提出了一种新型的基于光谱嵌入的算法，以同时学习单模式和二元交叉模式表示。尽管前者能够很好地保留每种方式的局部结构，但后者揭示了各种方式的隐藏模式。在第二步中，为了从信息性数据输入（图像和单词嵌入）到从第一步获得的二进制代码中学习映射函数，我们利用强大的CNN来获取图像，并提出基于CNN的深层体系结构来学习文本模式。对三个标准基准数据集的定量评估表明，所提出的DCSH方法始终超过其他最先进的方法。
This paper presents a novel framework, namely Deep Cross-modality Spectral Hashing (DCSH), to tackle the unsupervised learning problem of binary hash codes for efficient cross-modal retrieval. The framework is a two-step hashing approach which decouples the optimization into (1) binary optimization and (2) hashing function learning. In the first step, we propose a novel spectral embedding-based algorithm to simultaneously learn single-modality and binary cross-modality representations. While the former is capable of well preserving the local structure of each modality, the latter reveals the hidden patterns from all modalities. In the second step, to learn mapping functions from informative data inputs (images and word embeddings) to binary codes obtained from the first step, we leverage the powerful CNN for images and propose a CNN-based deep architecture to learn text modality. Quantitative evaluations on three standard benchmark datasets demonstrate that the proposed DCSH method consistently outperforms other state-of-the-art methods.
