2015/03/19 by Kai Li, Guo-Jun Qi, Guojun Qi +6
Computer Science · #Advanced Image and Video Retrieval Techniques #FOS: Computer and information sciences #Face and Expression Recognition #H.3.3 #I.2.6 #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Text and Document Classification Technologies #cs.IR #cs.LG
paper · pdf · doi:10.48550/arxiv.1503.05951
10 pages
arxiv created 2015/03/19 · openalex publication_date 2015/03/19 · arxiv updated 2015/03/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The era of Big Data has spawned unprecedented interests in developing hashing algorithms for efficient storage and fast nearest neighbor search. Most existing work learn hash functions that are numeric quantizations of feature values in projected feature space. In this work, we propose a novel hash learning framework that encodes feature's rank orders instead of numeric values in a number of optimal low-dimensional ranking subspaces. We formulate the ranking subspace learning problem as the optimization of a piece-wise linear convex-concave function and present two versions of our algorithm: one with independent optimization of each hash bit and the other exploiting a sequential learning framework. Our work is a generalization of the Winner-Take-All (WTA) hash family and naturally enjoys all the numeric stability benefits of rank correlation measures while being optimized to achieve high precision at very short code length. We compare with several state-of-the-art hashing algorithms in both supervised and unsupervised domain, showing superior performance in a number of data sets.