{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T01:48:26Z","timestamp":1698803306835},"reference-count":8,"publisher":"World Scientific Pub Co Pte Lt","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Semantic Computing"],"published-print":{"date-parts":[[2017,6]]},"abstract":"<jats:p> Learning to hash is receiving increasing research attention due to its effectiveness in addressing the large-scale similarity search problem. Most of the existing hashing algorithms are focused on learning hash functions in the form of numeric quantization of some projected feature space. In this work, we propose a novel hash learning method that encodes features\u2019 relative ordering instead of quantizing their numeric values in a set of low-dimensional ranking subspaces. We formulate the ranking-based hash learning problem as the optimization of a continuous probabilistic error function using softmax approximation and present an efficient learning algorithm to solve the problem. As a generalization of Winner-Take-All (WTA) hashing, the proposed algorithm naturally enjoys the numeric stability benefits of rank correlation measures while being optimized to achieve high precision with very compact code. Additionally, the proposed method can also be easily extended to nonlinear kernel spaces to discover ranking structures that can not be revealed in linear subspaces. We demonstrate through extensive experiments that the proposed method can achive competitive performances as compared to a number of state-of-the-art hashing methods. <\/jats:p>","DOI":"10.1142\/s1793351x17400074","type":"journal-article","created":{"date-parts":[[2017,6,22]],"date-time":"2017-06-22T23:41:14Z","timestamp":1498174874000},"page":"171-192","source":"Crossref","is-referenced-by-count":2,"title":["Semantic Image Retrieval with Feature Space Rankings"],"prefix":"10.1142","volume":"11","author":[{"given":"Kai","family":"Li","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Central Florida, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guo-Jun","family":"Qi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Central Florida, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Central Florida, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tuoerhongjiang","family":"Yusuph","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Central Florida, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kien A.","family":"Hua","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Central Florida, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2017,6,22]]},"reference":[{"key":"S1793351X17400074BIB002","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-008-0096-z"},{"key":"S1793351X17400074BIB003","volume-title":"Pattern Recognition and Machine Learning","author":"Bishop C. 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