Hashing techniques have been applied broadly in retrieval tasks due to their low storage requirements and high speed of processing. Many hashing methods based on a single view have been extensively studied for information retrieval. However, the representation capacity of a single view is insufficient and some discriminative information is not captured, which results in limited improvement. In this paper, we employ multiple views to represent images and texts for enriching the feature information. Our framework exploits the complementary information among multiple views to better learn the discriminative compact hash codes. A discrete hashing learning framework that jointly performs classifier learning and subspace learning is proposed to complete multiple search tasks simultaneously. Our framework includes two stages, namely a kernelization process and a quantization process. Kernelization aims to find a common subspace where multi-view features can be fused. The quantization stage is designed to learn discriminative unified hashing codes. Extensive experiments are performed on single-label datasets (WiKi and MMED) and multi-label datasets (MIRFlickr and NUS-WIDE), and the experimental results indicate the superiority of our method compared with the state-of-the-art methods.

中文翻译：

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学习基于多视图特征的判别性哈希码以进行跨模式检索

散列技术由于其低存储需求和高处理速度而被广泛应用于检索任务。对于信息检索，已经广泛研究了许多基于单个视图的哈希方法。但是，单个视图的表示能力不足，无法捕获某些区分性信息，因此改进效果有限。在本文中，我们使用多个视图来表示图像和文本，以丰富特征信息。我们的框架利用多个视图之间的互补信息来更好地学习区分性紧凑型哈希码。提出了一种联合执行分类器学习和子空间学习的离散哈希学习框架，以同时完成多个搜索任务。我们的框架包括两个阶段，即内核化过程和量化过程。内核化的目的是找到可以融合多视图功能的公共子空间。量化阶段旨在学习判别式统一哈希码。在单标签数据集（WiKi和MMED）和多标签数据集（MIRFlickr和NUS-WIDE）上进行了广泛的实验，实验结果表明我们的方法与最新方法相比具有优越性。