A Novel Deep Hashing Method with Top Similarity for Image Retrieval

Due to the advantages of retrieval speed and storage space, deep hashing methods have become a research hotspot in the field of large-scale image retrieval. Most of existing deep hashing methods pay close attention to similarity between images without images at the top of the ranking list similar to query targets. In the paper, a novel deep hashing model is proposed to preserve top images similar to the query images and optimize the quality of hash codes for image retrieval. Specifically, the optimized AlexNet is utilized to extract discriminative image representations and learn hashing functions simultaneously. The loss function based on acceleration strategy is designed to ensure similarity between returned images at the top of the ranking list and query images. In addition, we implement the model training in a batch-process fashion to low the image storage. Moreover, our extensive experiments on standard benchmarks demonstrate that our method outperforms several state-of-the-art deep hashing methods.