Multi-scale Adaptive Feature Fusion Hashing for Image Retrieval

Abstract The hash algorithm has the characteristics of high computational speed and low memory consumption, making it well-suited for massive image search tasks. Currently, most methods rely on deep learning for end-to-end feature extraction and hash encoding. These methods use the last layer feature of the model as the semantic feature of the hash encoding image. However, mainstream research has not realized that the features of different stages of the network contain rich image semantic information, which all affect the performance of retrieval. Based on this, we propose a multi-scale adaptive feature fusion hash image retrieval method, which mines more detailed information about the image by introducing adaptive feature fusion modules at different stages of the network, and incorporates shallow features in the final extracted features to help the model understand the image content. In addition, to maintain the similarity of the generated image hash codes and reduce errors in the process of transitioning from original features to hash codes, similarity loss and quantization loss are applied, while fully utilizing the supervised information of dataset labels to get high-quality hash codes. Experimental proof conducted on the CIFAR-10 and NUS-WIDE datasets that this method outperforms other deep learning-based hash image retrieval methods.