{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T23:06:30Z","timestamp":1746313590179},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>Recently, similarity-preserving hashing methods have been extensively studied for large-scale image retrieval. Compared with unsupervised hashing, supervised hashing methods for labeled data have usually better performance by utilizing semantic label information. Intuitively, for unlabeled data, it will improve the performance of unsupervised hashing methods if we can first mine some supervised semantic 'label information' from unlabeled data and then incorporate the 'label information' into the training process. Thus, in this paper, we propose a novel Object Detection based Deep Unsupervised Hashing method (ODDUH). Specifically, a pre-trained object detection model is utilized to mining supervised 'label information', which is used to guide the learning process to generate high-quality hash codes. Extensive experiments on two public datasets demonstrate that the proposed method outperforms the state-of-the-art unsupervised hashing methods in the image retrieval task.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/500","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"3606-3612","source":"Crossref","is-referenced-by-count":6,"title":["Object Detection based Deep Unsupervised Hashing"],"prefix":"10.24963","author":[{"given":"Rong-Cheng","family":"Tu","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, Beijing Institute of Technology, China"},{"name":"CETC Big Data Research Institute, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xian-Ling","family":"Mao","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Beijing Institute of Technology, China"},{"name":"Zhijiang Lab, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo-Si","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Beijing Institute of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shu-ying","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Beijing Institute of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2019","name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","start":{"date-parts":[[2019,8,10]]},"theme":"Artificial Intelligence","location":"Macao, China","end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:49:44Z","timestamp":1564285784000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/500"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/500","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}