Multi-view hashing has gained considerable research attention in efficient multimedia studies due to its promising performance on heterogeneous data from various sources. However, its application in discriminative hash codes learning remains challenging as it fails to efficiently capture preferable components from multiple representations. In this work, we propose a novel discriminative multi-view hashing framework, dubbed flexible discrete multi-view hashing, in conjunction with collective latent feature learning by combining multiple views of data and consistent hash codes learning by fusing visual features and flexible semantics. Specifically, an adaptive multi-view analysis dictionary learning model is developed to skillfully combine diverse representations into an established common latent feature space where the complementary properties of different views are well explored based on an automatic multi-view weighting strategy. Moreover, we introduce a collaborative learning scheme to jointly encode the visual and semantic embeddings into an aligned consistent Hamming space, which can effectively mitigate the visual-semantic gap. Particularly, we employ the correntropy induced regularization to improve the robustness of the formulated flexible semantics. An efficient learning algorithm is proposed to solve the optimization problem. Extensive experiments show the state-of-art performance of the proposed method on several benchmark datasets.

由于多视图散列技术在处理来自各种来源的异构数据方面的良好前景，因此在有效的多媒体研究中获得了相当多的研究关注。但是，由于它无法有效地从多种表示中捕获可取的成分，因此在区分性哈希码学习中的应用仍然具有挑战性。在这项工作中，我们提出了一种新颖的区分性多视图哈希算法框架，称为灵活离散多视图哈希算法，结合了通过将数据的多个视图结合在一起的集体潜在特征学习和通过融合视觉特征和灵活语义的一致哈希码学习。特别，自适应多视图分析字典学习模型被开发出来，可以将各种表示形式巧妙地组合到已建立的共同潜在特征空间中，其中基于自动多视图加权策略，可以很好地探索不同视图的互补属性。此外，我们引入了一种协作学习方案，以将视觉和语义嵌入联合编码到对齐的一致汉明空间中，从而可以有效地缓解视觉语义鸿沟。特别是，我们采用了熵变引起的正则化，以提高所提出的灵活语义的鲁棒性。提出了一种有效的学习算法来解决优化问题。大量实验表明，该方法在多个基准数据集上具有最先进的性能。