Adaptive Confidence Multi-View Hashing for Multimedia Retrieval
This work addresses noise reduction and confidence fusion in multimedia retrieval, offering an incremental improvement over existing multi-view hashing techniques.
The paper tackles the problem of noise and lack of confidence learning in multi-view hashing for multimedia retrieval by proposing ACMVH, which uses confidence networks to filter noise and adaptively fuse views, resulting in a maximum performance increase of 3.24% over state-of-the-art methods on public datasets.
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.