Depth Image Hashing Algorithm Based on Local Global Feature Fusion

In recent years, deep learning-based hashing techniques have garnered significant attention within the academic community, owing to their impressive performance achievements. However, the current deep hashing method is limited by its use of neural networks cannot make good use of the important feature information of the image, thus limiting the retrieval accuracy. In this paper, we design a two-channel feature extraction network called the local and global feature fusion network (LGDH). Specifically, the features extracted by visual geometry group (VGG) network and con-volution-enhanced image transformer (CeiT) network are deeply orthogonal fusion of local and global features, which solves the problem of lack of semantic correlation in existing deep hash methods. To fully harness the insights gained from previous hash function learning, we employ long short-term memory (LSTM) decoder to proficiently shape and create a resilient public binary code space. Through detailed experiments, our proposed method has achieved the Sota level in three public datasets CIFAR10, NUS-WIDE and MIRFLICKR compared with multiple cutting-edge algorithms, which can prove the effectiveness of our pro-posed method.