Consistent coding guided domain adaptation retrieval

Domain adaptation retrieval (DAR) has become a research hotspot. The current DAR methods have the following problems. 1) They fail to learn a distinguishable retrieval pool during training, which can be better used for retrieval. 2) They ignore the similarity imbalance problem, leading to less attention to similar relationships than dissimilar relationships. 3) There is quantization error caused by classifier, which limits the discriminability of hash codes. To tackle these problems, this paper proposed a consistent coding guided domain adaptation retrieval (CCG) method which simultaneously involves two modules, including consistent hash codes learning and hash function learning. The former adaptively learns distinguishable and domain-consistent hash codes by composing two novel terms: the label-based iterative quantization term and the probability weighted similarity preserving term. The first term uses a classifier to construct the label-based quantization, and introduces an orthogonal rotation matrix to reduce the quantization error. This brings the classification result to the nearest vertex of the Hamming hypercube, thus improving the discriminability of the hash codes. The second term constructs similarity matrices for both intra-domain and inter-domain samples according to their labels, and preserves the similarity relationship between hash codes. In addition, it dynamically and adaptively adjusts the weights of preserving the similar and dissimilar relationship to alleviate the similarity unbalance problem. This further enhances the discriminability and the domain-consistency of the hash codes. Extensive experiments on various datasets demonstrate that the proposed CCG achieves the state-of-the-art performance. The source code is available at https://github.com/SkyHappyHu/CCG.