Hashing could significantly accelerate large-scale image search by transforming the high-dimensional features into binary Hamming space, where efficient similarity search can be achieved with very fast Hamming distance computation and extremely low storage cost. As an important branch of hashing methods, multi-view hashing takes advantages of multiple features from different views for binary hash learning. However, existing multi-view hashing methods are either based on shallow models which fail to fully capture the intrinsic correlations of heterogeneous views, or unsupervised deep models which suffer from insufficient semantics and cannot effectively exploit the complementarity of view features. In this paper, we propose a novel Deep Collaborative Multi-view Hashing (DCMVH) method to deeply fuse multi-view features and learn multi-view hash codes collaboratively under a deep architecture. DCMVH is a new deep multi-view hash learning framework. It mainly consists of 1) multiple view-specific networks to extract hidden representations of different views, and 2) a fusion network to learn multi-view fused hash code. DCMVH associates different layers with instance-wise and pair-wise semantic labels respectively. In this way, the discriminative capability of representation layers can be progressively enhanced and meanwhile the complementarity of different view features can be exploited effectively. Finally, we develop a fast discrete hash optimization method based on augmented Lagrangian multiplier to efficiently solve the binary hash codes. Experiments on public multi-view image search datasets demonstrate our approach achieves substantial performance improvement over state-of-the-art methods.

中文翻译：

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用于大规模图像搜索的深度协作多视图散列

通过将高维特征转换为二进制汉明空间，散列可以显着加速大规模图像搜索，在该空间中，可以通过非常快速的汉明距离计算和极低的存储成本来实现有效的相似性搜索。作为哈希方法的重要分支，多视图哈希利用来自不同视图的多个功能进行二进制哈希学习。但是，现有的多视图哈希方法要么基于无法完全捕获异构视图的内在相关性的浅层模型，要么基于语义不足且无法有效利用视图特征的互补性的无监督深层模型。在本文中，我们提出了一种新颖的深度协作多视图哈希（DCMVH）方法，以深度融合多视图功能并在深度架构下协作学习多视图哈希码。DCMVH是一种新的深度多视图哈希学习框架。它主要包括1）多个特定于视图的网络以提取不同视图的隐藏表示，以及2）融合网络以学习多视图融合的哈希码。DCMVH将不同的层分别与按实例和按对语义标签关联。这样，可以逐渐增强表示层的判别能力，同时可以有效地利用不同视图特征的互补性。最后，我们开发了一种基于增强拉格朗日乘数的快速离散哈希优化方法，以有效地解决二进制哈希码。