2018/10/01 by Xingbo Liu, Liu, Xingbo, Xiushan Nie +3
Computer Science · #Advanced Image and Video Retrieval Techniques #Advanced Neural Network Applications #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Multimedia (cs.MM) #Video Surveillance and Tracking Methods #cs.DS #cs.MM
paper · pdf · doi:10.48550/arxiv.1810.00644
10 pages, 6 figures
arxiv created 2018/10/01 · openalex publication_date 2018/10/01 · arxiv updated 2018/10/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Hashing has been widely used for efficient similarity search based on its query and storage efficiency. To obtain better precision, most studies focus on designing different objective functions with different constraints or penalty terms that consider neighborhood information. In this paper, in contrast to existing hashing methods, we propose a novel generalized framework called fusion hashing (FH) to improve the precision of existing hashing methods without adding new constraints or penalty terms. In the proposed FH, given an existing hashing method, we first execute it several times to get several different hash codes for a set of training samples. We then propose two novel fusion strategies that combine these different hash codes into one set of final hash codes. Based on the final hash codes, we learn a simple linear hash function for the samples that can significantly improve model precision. In general, the proposed FH can be adopted in existing hashing method and achieve more precise and stable performance compared to the original hashing method with little extra expenditure in terms of time and space. Extensive experiments were performed based on three benchmark datasets and the results demonstrate the superior performance of the proposed framework