Discrete Hashing with Deep Neural Network
This work addresses the challenge of efficient image retrieval for large-scale applications, representing an incremental improvement over existing deep hashing methods.
The paper tackles the problem of learning binary hash codes for large-scale image search by proposing a deep neural network method that efficiently solves binary constraints without relaxation and incorporates criteria like similarity preservation, balance, and independence, achieving state-of-the-art performance on three benchmark datasets.
This paper addresses the problem of learning binary hash codes for large scale image search by proposing a novel hashing method based on deep neural network. The advantage of our deep model over previous deep model used in hashing is that our model contains necessary criteria for producing good codes such as similarity preserving, balance and independence. Another advantage of our method is that instead of relaxing the binary constraint of codes during the learning process as most previous works, in this paper, by introducing the auxiliary variable, we reformulate the optimization into two sub-optimization steps allowing us to efficiently solve binary constraints without any relaxation. The proposed method is also extended to the supervised hashing by leveraging the label information such that the learned binary codes preserve the pairwise label of inputs. The experimental results on three benchmark datasets show the proposed methods outperform state-of-the-art hashing methods.