TY - GEN
T1 - Similar-Video Retrieval via Learned Exemplars and Time-Warped Alignment
AU - Horie, Teruki
AU - Moriwaki, Masafumi
AU - Yokote, Ryota
AU - Ninomiya, Shota
AU - Shikano, Akihiro
AU - Matsuyama, Yasuo
PY - 2014
Y1 - 2014
N2 - New learning algorithms and systems for retrieving similar videos are presented. Each query is a video itself. For each video, a set of exemplars is machine-learned by new algorithms. Two methods were tried. The first and main one is the time-bound affinity propagation. The second is the harmonic competition which approximates the first. In the similar-video retrieval, the number of exemplar frames is variable according to the length and contents of videos. Therefore, each exemplar possesses responsible frames. By considering this property, we give a novel similarity measure which contains the Levenshtein distance (L-distance) as its special case. This new measure, the M-distance, is applicable to both of global and local alignments for exemplars. Experimental results in view of precision-recall curves show creditable scores in the region of interest.
AB - New learning algorithms and systems for retrieving similar videos are presented. Each query is a video itself. For each video, a set of exemplars is machine-learned by new algorithms. Two methods were tried. The first and main one is the time-bound affinity propagation. The second is the harmonic competition which approximates the first. In the similar-video retrieval, the number of exemplar frames is variable according to the length and contents of videos. Therefore, each exemplar possesses responsible frames. By considering this property, we give a novel similarity measure which contains the Levenshtein distance (L-distance) as its special case. This new measure, the M-distance, is applicable to both of global and local alignments for exemplars. Experimental results in view of precision-recall curves show creditable scores in the region of interest.
KW - Exemplar
KW - M-distance
KW - Numerical label
KW - Similar-video retrieval
KW - Time-bound affinity propagation
UR - http://www.scopus.com/inward/record.url?scp=84910019740&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84910019740&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84910019740
SN - 9783319126425
VL - 8836
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 85
EP - 94
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
T2 - 21st International Conference on Neural Information Processing, ICONIP 2014
Y2 - 3 November 2014 through 6 November 2014
ER -