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Therefore, automatic analysis of these vehicle images is helpful for traffic flow management, criminal investigations and vehicle inspections. Vehicle matching, which aims to determine whether two input images depict an identical vehicle, is one of the core tasks in vehicle analysis. Recent relevant studies have focused on local feature extraction instead of global extraction, since local details can provide crucial cues to distinguish between cars. However, these methods do not select local features; that is, they do not assign weights to local features. Therefore, in this research, we systematically study the vehicle matching task, and present a novel annotation\u2010free local\u2010based deep learning method called Adaptive super\u2010pixel discriminative feature\u2010selective learning (ASDFL) to address this issue. In ASDFL, vehicle images are segmented into clusters of super\u2010pixels of similar size by considering the location and colour similarities of pixels without using any component\u2010level annotation. These super\u2010pixels are deemed to be the virtual components of vehicles. Moreover, a convolutional neural network is used to extract the deep features of these virtual components. Thereafter, an instance\u2010specific mask generation module driven by the extracted global features is enhanced to produce a mask to select the most distinctive virtual components of each vehicle image pair in the feature space. Finally, the vehicle matching task is accomplished by classifying the selected virtual component features of each imaged vehicle pair. Extensive experiments on two popular vehicle identification benchmarks demonstrate that our method is 1.57% and 0.8% more accurate than the previous baselines in a vehicle matching task on the VeRi and VehicleID datasets, respectively, which demonstrates the effectiveness of our method.<\/jats:p>","DOI":"10.1111\/exsy.13144","type":"journal-article","created":{"date-parts":[[2022,10,4]],"date-time":"2022-10-04T22:04:40Z","timestamp":1664921080000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ASDFL: An adaptive super\u2010pixel discriminative feature\u2010selective learning for vehicle matching"],"prefix":"10.1111","volume":"40","author":[{"given":"Rong","family":"Qin","sequence":"first","affiliation":[{"name":"School of Big Data and Software Engineering Chongqing University  Chongqing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huanhuan","family":"Lv","sequence":"additional","affiliation":[{"name":"School of Big Data and Software Engineering Chongqing University  Chongqing China"},{"name":"Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University  Chongqing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Big Data and Software Engineering Chongqing University  Chongqing China"},{"name":"Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University  Chongqing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luwen","family":"Huangfu","sequence":"additional","affiliation":[{"name":"Fowler College of Business San Diego State University  San Diego California USA"},{"name":"Center for Human Dynamics in the Mobile Age San Diego State University  San Diego California USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheng","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Big Data and Software Engineering Chongqing University  Chongqing China"},{"name":"Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University  Chongqing China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,10,4]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.120"},{"key":"e_1_2_9_3_1","first-page":"176","article-title":"Multiple inductive loop detectors for intelligent transportation systems applications: Ramp metering, vehicle re\u2010identification and lane change monitoring systems","author":"Ali S. 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