2021/03/17 by Ming Zhang, Hong Yan, Zhang, Ming +1
Computer Science · #Advanced Image and Video Retrieval Techniques #Advanced Neural Network Applications #Artificial intelligence #Benchmark (surveying) #Class (philosophy) #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Deep learning #Discriminative model #FOS: Computer and information sciences #Hash function #Hash table #Image (mathematics) #Image retrieval #Information Retrieval (cs.IR) #Locality-sensitive hashing #Machine learning #Nearest neighbor search #Pairwise comparison #Pattern recognition (psychology) #Similarity (geometry) #Video Surveillance and Tracking Methods #cs.CV #cs.IR
paper · pdf · doi:10.48550/arxiv.2103.09442
published in arXiv (Cornell University) (Cornell University) · Accepted at ICPR'20
arxiv created 2021/03/17 · openalex publication_date 2021/03/17 · arxiv updated 2021/03/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Deep supervised hashing for image retrieval has attracted researchers' attention due to its high efficiency and superior retrieval performance. Most existing deep supervised hashing works, which are based on pairwise/triplet labels, suffer from the expensive computational cost and insufficient utilization of the semantics information. Recently, deep classwise hashing introduced a classwise loss supervised by class labels information alternatively; however, we find it still has its drawback. In this paper, we propose an improved deep classwise hashing, which enables hashing learning and class centers learning simultaneously. Specifically, we design a two-step strategy on center similarity learning. It interacts with the classwise loss to attract the class center to concentrate on the intra-class samples while pushing other class centers as far as possible. The centers similarity learning contributes to generating more compact and discriminative hashing codes. We conduct experiments on three benchmark datasets. It shows that the proposed method effectively surpasses the original method and outperforms state-of-the-art baselines under various commonly-used evaluation metrics for image retrieval.