02133nam a2200373   4500000000100000000000100001008004100002260002000043653003600063653002800099653001600127653002400143653002500167653001900192653002000211653002800231653001700259653001300276653002100289653002200310653001500332653002600347100001400373700001300387700001400400700001600414700001500430700002000445245006400465856015200529490001500681520102100696020004201717       2017                            d  bSpringer Verlag10aClassification (of information)10aClassification accuracy10aFolk dances10aIntelligent systems10aK-NN classifications10aMotion Vectors10aMovement phrase10aNearest neighbor search10aTable lookup10aTeaching10aVideo Annotation10aVideo annotations10aVietnamese10aVietnamese folk dance1 aC. Ma-Thi1 aK. Tabia1 aS. Lagrue1 aH. Le-Thanh1 aD. Bui-The1 aT. Nguyen-Thanh00aAnnotating movement phrases in Vietnamese folk dance videos  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85026310129&doi=10.1007%2f978-3-319-60045-1_1&partnerID=40&md5=52e7df4cee3c5098575214431f97a6ab0 v10351 LNCS3 aThis paper aims at the annotation of movement phrases in Vietnamese folk dance videos that were mainly gathered, stored and used in teaching at art schools and in preserving cultural intangible heritages (performed by different famous folk dance masters). We propose a framework of automatic movement phrase annotation, in which the motion vectors are used as movement phrase features. Movement phrase classification can be carried out, based on dancer’s trajectories. A deep investigation of Vietnamese folk dance gives an idea of using optical flow as movement phrase features in movement phrase detection and classification. For the richness and usefulness in annotation of Vietnamese folk dance, a lookup table of movement phrase descriptions is defined. In initial experiments, a sample movement phrase dataset is built up to train k-NN classification model. Experiments have shown the effectiveness of the proposed framework of automatic movement phrase annotation with classification accuracy at least 88\%.  a03029743 (ISSN); 9783319600444 (ISBN)