{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T17:16:22Z","timestamp":1764954982848,"version":"3.46.0"},"reference-count":0,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2012,2,29]],"date-time":"2012-02-29T00:00:00Z","timestamp":1330473600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,3]]},"abstract":"<jats:title>Abstract.<\/jats:title>\n                  <jats:p>\n                    NormalBoost is a new boosting algorithm which is capable of classifying a\nmulti-dimensional binary class dataset. It adaptively combines several weak\nclassifiers to form a strong classifier. Unlike many boosting algorithms\nwhich have high computation and memory complexities, NormalBoost is capable\nof classification with low complexity.\nThe purpose of this paper is to present NormalBoost as a framework which\nestablishes a platform to solve classification problems. The approach was\ntested with a dataset which was extracted automatically from real-world\ntraffic sign images. The dataset contains both images of traffic sign\nborders and speed limit pictograms. This framework involves the computation\nof Haar-like features of these images and then employs the NormalBoost classifier to classify these traffic signs. The total number of images which\nwere classified was 6500 binary images. A\n                    <jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"jisys-2012-0001_eq1.png\"\/>\n                    -fold validation was invoked to\ncheck the validity of the classification which resulted in a classification\nrate of 98.4% and 98.9% being achieved for these two databases. This\nframework is distinguished by its invariance to in-plane rotation of the\nimages under consideration. Experiments show that the classification rate\nremains almost constant when traffic sign images with different angles of\nrotations were tested.\n                  <\/jats:p>","DOI":"10.1515\/jisys-2012-0001","type":"journal-article","created":{"date-parts":[[2012,3,21]],"date-time":"2012-03-21T09:53:27Z","timestamp":1332323607000},"page":"25-43","source":"Crossref","is-referenced-by-count":0,"title":["Classification with NormalBoost: Case Study Traffic Sign Classification"],"prefix":"10.1515","volume":"21","author":[{"given":"Hasan","family":"Fleyeh","sequence":"first","affiliation":[{"name":"Computer Science Department, School for Technology and Business Studies, Dalarna University, R\u00f6dav\u00e4gen 3, 78188 Borl\u00e4nge, Sweden"}]},{"given":"Erfan","family":"Davami","sequence":"additional","affiliation":[{"name":"Computer Science Department, School for Technology and Business Studies, Dalarna University, R\u00f6dav\u00e4gen 3, 78188 Borl\u00e4nge, Sweden"}]}],"member":"374","published-online":{"date-parts":[[2012,2,29]]},"container-title":["Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2012-0001\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2012-0001\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T17:11:42Z","timestamp":1764954702000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2012-0001\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,2,29]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2012,2,29]]},"published-print":{"date-parts":[[2012,3]]}},"alternative-id":["10.1515\/jisys-2012-0001"],"URL":"https:\/\/doi.org\/10.1515\/jisys-2012-0001","relation":{},"ISSN":["2191-026X","0334-1860"],"issn-type":[{"type":"electronic","value":"2191-026X"},{"type":"print","value":"0334-1860"}],"subject":[],"published":{"date-parts":[[2012,2,29]]}}}