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Recently, although some modified schemes were presented to improve the proposal localization quality, the mechanism of enhancing the performance is still an open problem. In this paper, Adaptive weighted binary normed gradients plus (AWBING Plus) algorithm is proposed, based on the BING method, which replaces the support vector machine (SVM) with adaptive weighted extreme learning machine (Adaptive WELM) to reduce the number of proposals, as well as comparable performance, by using the multi-thresholding straddling expansion (MTSE) as the post-processing stage to enhance the localization quality. We explain the methodology of WELM applied to BING, and analyzed the effect of the improved WELM algorithm, which is named Adaptive WELM. The experimental results from PASCAL VOC2007, Microsoft COCO2014 and ILSVRC2013 show that the proposed approach achieved superior performance compared with other advanced methods on generic object proposal generation, and it runs faster as well.<\/jats:p>","DOI":"10.3233\/jifs-18810","type":"journal-article","created":{"date-parts":[[2019,5,31]],"date-time":"2019-05-31T11:40:29Z","timestamp":1559302829000},"page":"6685-6701","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["AWBING plus algorithm for generic object proposal generation"],"prefix":"10.1177","volume":"36","author":[{"given":"Qian","family":"Liu","sequence":"first","affiliation":[{"name":"School of Mechanical Electronic &amp; Information Engineering, China University of Mining &amp; Technology, Beijing, China"},{"name":"School of Information Engineering, Ningxia University, Ningxia, China"}]},{"given":"Feng","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mechanical Electronic &amp; Information Engineering, China University of Mining &amp; Technology, Beijing, China"}]},{"given":"Ce","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Electronic &amp; Information Engineering, China University of Mining &amp; Technology, Beijing, China"}]}],"member":"179","published-online":{"date-parts":[[2019,5,29]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"73","article-title":"What is an object?","author":"Alexe B.","year":"2010","unstructured":"AlexeB., DeselaersT., FerrariV., What is an object? 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, pp. 73\u201380.","journal-title":"2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.28"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.49"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF01396750"},{"key":"e_1_3_2_6_2","first-page":"235","volume-title":"Proceedings of the 23rd International Conference on Neural Information Processing Systems - Volume 1","author":"Blaschko M.B.","year":"2010","unstructured":"BlaschkoM.B., VedaldiA., ZissermanA., Simultaneous object detection and ranking with weak supervision, in: Proceedings of the 23rd International Conference on Neural Information Processing Systems - Volume 1, Curran Associates Inc., Vancouver, British Columbia, Canada, 2010, pp. 235\u2013243."},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.89"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.231"},{"key":"e_1_3_2_9_2","first-page":"1","article-title":"S-CNN: Subcategory-aware convolutional networks for object detection","author":"Chen T.","year":"2018","unstructured":"ChenT., LuS., FanJ., S-CNN: Subcategory-aware convolutional networks for object detection, IEEE Transactions on Pattern Analysis and Machine Intelligence PP (2018), 1\u20131.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.09.045"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.414"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.177"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.5244\/C.23.91"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.122"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4"},{"key":"e_1_3_2_16_2","first-page":"1871","article-title":"LIBLINEAR: A Library for Large Linear Classification","volume":"9","author":"Fan R.-E.","year":"2010","unstructured":"FanR.-E., ChangK.-W., HsiehC.-J., WangX.-R., LinC.-J., LIBLINEAR: A Library for Large Linear Classification, J. 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