<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tian, Yonghong; *Huang, Tiejun; Jiang, Menglin; Gao, Wen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Video copy-detection and localization with a scalable cascading framework</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Multimedia</style></secondary-title><short-title><style face="normal" font="default" size="100%">Video copy-detection and localization with a scalable cascading framework</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">multimedia; scalable cascading; Sequence matching; Soft threshold; TRECVID-CBCD; Video copy detection</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013/3</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.engineeringvillage2.org/search/results/fulltext.url?docID=cpx_6e3d6014128974af0M200c2061377553</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">72-86</style></pages><isbn><style face="normal" font="default" size="100%">1070-986X</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">For video copy detection, no single audio-visual feature, or single detector based on several features, can work well for all transformations. This article proposes a novel video copy-detection and localization approach with scalable cascading of complementary detectors and multiscale sequence matching. In this cascade framework, a soft-threshold learning algorithm is utilized to estimate the optimal decision thresholds for detectors, and a multiscale sequence matching method is employed to precisely locate copies using a 2D Hough transform and multigranularities similarity evaluation. Excellent performance on the TRECVID-CBCD 2011 benchmark dataset shows the effectiveness and efficiency of the proposed approach.</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><section><style face="normal" font="default" size="100%">72</style></section></record></records></xml>