The multi-view hash method converts heterogeneous data from multiple views into binary hash codes, which is one of the critical technologies in multimedia retrieval. However, the current methods mainly explore the complementarity among multiple views while lacking confidence learning and fusion. Moreover, in practical application scenarios, the single-view data contain redundant noise. To conduct the confidence learning and eliminate unnecessary noise, we propose a novel Adaptive Confidence Multi-View Hashing (ACMVH) method. First, a confidence network is developed to extract useful information from various single-view features and remove noise information. Furthermore, an adaptive confidence multi-view network is employed to measure the confidence of each view and then fuse multi-view features through a weighted summation. Lastly, a dilation network is designed to further enhance the feature representation of the fused features. To the best of our knowledge, we pioneer the application of confidence learning into the field of multimedia retrieval. Extensive experiments on two public datasets show that the proposed ACMVH performs better than state-of-the-art methods (maximum increase of 3.24%). The source code is available at https://github.com/HackerHyper/ACMVH.

翻译：多视图哈希方法将来自多个视图的异构数据转化为二进制哈希码，是多媒体检索中的关键技术之一。然而，现有方法主要探索多个视图间的互补性，在置信度学习与融合方面有所欠缺。此外，在实际应用场景中，单视图数据包含冗余噪声。为了进行置信度学习并消除不必要的噪声，我们提出了一种新颖的自适应置信度多视图哈希（ACMVH）方法。首先，我们构建了一个置信度网络，用于从各种单视图特征中提取有用信息并去除噪声信息。其次，采用自适应置信度多视图网络来衡量每个视图的置信度，并通过加权求和融合多视图特征。最后，设计了一个膨胀网络，以进一步增强融合后特征的特征表示能力。据我们所知，我们率先将置信度学习应用到多媒体检索领域。在两个公共数据集上的大量实验表明，所提出的ACMVH方法性能优于当前最先进的方法（最高提升3.24%）。源代码可在https://github.com/HackerHyper/ACMVH获取。