en
0.25
0.5
0.75
1.25
1.5
1.75
2
Knowledge Discovery of Artistic Influences: A Metric Learning Approach
Published on Aug 08, 20142671 Views
We approach the challenging problem of discovering influences between painters based on their fine-art paintings. In this work, we focus on comparing paintings of two painters in terms of visual sim
Related categories
Chapter list
Knowledge Discovery of artistic influences: A metric learning approach00:00
Presenters00:13
Painting00:37
Visual language of art01:07
What influence artists?01:43
Painting - 102:12
Painting - 202:38
Painting - 302:39
What is art influence?02:46
How do art historians determine influence?03:06
What do computer scientists know about art anyway? - 103:58
What do computer scientists know about art anyway? - 204:35
Our focus05:12
Prior research05:45
Framework06:48
What kinds of similarities are we looking for?06:51
Representation07:13
Dataset08:06
Style classification - 108:26
Style classification - 208:38
Style classification - 309:04
Style classification - 409:47
Style classification - 510:13
Overall summarized results11:40
Accuracy12:25
Style classification - 612:42
Painting similarity - 112:56
Painting similarity - 213:17
Painting similarity - 313:22
Painting similarity - 413:29
Painting similarity - 513:54
Painting similarity - 614:27
New study: Metric learning approach15:11
Metric learning - 115:27
Metric learning - 215:45
Style classification - 715:52
Influence inference framework16:19
Problem formulation16:26
Artist distance16:31
Artist influence graph17:17
Retrival across styles17:33
Painting - 217:51
Painting - 318:16
Most similar paintings across styles18:28
Painting - 318:38
Painting -418:46
Painting - 518:50
Painting - 618:52
Evaluation - influence discovery - 118:53
Evaluation - influence discovery - 219:00
Recall - 119:25
Recall - 219:30
Recall (metric learning) - 119:33
Recall (metric learning) - 219:51
Recall (metric learning) - 320:02
Recall (metric learning) - 420:03
Recall (metric learning) - 520:39
Map of artists - 120:41
Map of artists - 220:43
Map of artists - 321:08
Map of artists - 421:15
Conclusions21:16