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 this https URL

提出了一种新颖的自适应置信度多视图哈希方法 (ACMVH)，利用置信度学习和融合技术，通过消除噪音和提升特征表示来改进多媒体检索。实验证明，ACMVH 方法在两个公共数据集上表现优于现有的方法 (最大增加了 3.24%)。