2016/11/30 by Gou Koutaki, Koutaki, Gou, Keiichiro Shirai +3 · 1 citation
Computer Science · #Advanced Image and Video Retrieval Techniques #Algorithms and Data Compression #Caching and Content Delivery #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multimedia (cs.MM) #cs.CV #cs.LG #cs.MM
paper · pdf · doi:10.48550/arxiv.1611.10017
12 pages
arxiv created 2016/11/30 · openalex publication_date 2016/11/30 · arxiv updated 2016/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we propose a learning-based supervised discrete hashing method. Binary hashing is widely used for large-scale image retrieval as well as video and document searches because the compact representation of binary code is essential for data storage and reasonable for query searches using bit-operations. The recently proposed Supervised Discrete Hashing (SDH) efficiently solves mixed-integer programming problems by alternating optimization and the Discrete Cyclic Coordinate descent (DCC) method. We show that the SDH model can be simplified without performance degradation based on some preliminary experiments; we call the approximate model for this the "Fast SDH" (FSDH) model. We analyze the FSDH model and provide a mathematically exact solution for it. In contrast to SDH, our model does not require an alternating optimization algorithm and does not depend on initial values. FSDH is also easier to implement than Iterative Quantization (ITQ). Experimental results involving a large-scale database showed that FSDH outperforms conventional SDH in terms of precision, recall, and computation time.