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：

---

基于多视图特征的跨模态检索的判别哈希码学习

由于其低存储要求和高处理速度，散列技术已广泛应用于检索任务。许多基于单个视图的散列方法已被广泛研究用于信息检索。然而，单个视图的表示能力不足，并且没有捕捉到一些判别信息，导致改进有限。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有区别的紧凑哈希码。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，即核化过程和量化过程。核化旨在找到可以融合多视图特征的公共子空间。量化阶段旨在学习有区别的统一哈希码。在单标签数据集（WiKi 和 MMED）和多标签数据集（MIRFlickr 和 NUS-WIDE）上进行了大量实验，实验结果表明我们的方法与最先进的方法相比具有优越性。