{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T13:15:18Z","timestamp":1767705318336},"reference-count":24,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2014,5]]},"abstract":"<jats:p> In this paper, a novel method termed Multi-Instance Dictionary Learning (MIDL) is presented for detecting abnormal events in crowded video scenes. With respect to multi-instance learning, each event (video clip) in videos is modeled as a bag containing several sub-events (local observations); while each sub-event is regarded as an instance. The MIDL jointly learns a dictionary for sparse representations of sub-events (instances) and multi-instance classifiers for classifying events into normal or abnormal. We further adopt three different multi-instance models, yielding the Max-Pooling-based MIDL (MP-MIDL), Instance-based MIDL (Inst-MIDL) and Bag-based MIDL (Bag-MIDL), for detecting both global and local abnormalities. The MP-MIDL classifies observed events by using bag features extracted via max-pooling over sparse representations. The Inst-MIDL and Bag-MIDL classify observed events by the predicted values of corresponding instances. The proposed MIDL is evaluated and compared with the state-of-the-art methods for abnormal event detection on the UMN (for global abnormalities) and the UCSD (for local abnormalities) datasets and results show that the proposed MP-MIDL and Bag-MIDL achieve either comparable or improved detection performances. The proposed MIDL method is also compared with other multi-instance learning methods on the task and superior results are obtained by the MP-MIDL scheme. <\/jats:p>","DOI":"10.1142\/s0129065714300101","type":"journal-article","created":{"date-parts":[[2014,1,6]],"date-time":"2014-01-06T03:56:30Z","timestamp":1388980590000},"page":"1430010","source":"Crossref","is-referenced-by-count":24,"title":["MULTI-INSTANCE DICTIONARY LEARNING FOR DETECTING ABNORMAL EVENTS IN SURVEILLANCE VIDEOS"],"prefix":"10.1142","volume":"24","author":[{"given":"JING","family":"HUO","sequence":"first","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"YANG","family":"GAO","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"WANQI","family":"YANG","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"HUJUN","family":"YIN","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, The University of Manchester, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2014,2,19]]},"reference":[{"key":"rf4","author":"Yang W.","journal-title":"Comput. Vis. Image Understand."},{"key":"rf5","unstructured":"H.\u00a0Lee, Advances in Neural Information Processing Systems\u00a019, eds. 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