Name
Abstract | ||
|---|---|---|
The apparent distance of the camera from the subject of a filmed scene, namely shot scale, is one of the prominent formal features of any filmic product, endowed with both stylistic and narrative functions. In this work we propose to use Convolutional Neural Networks for the automatic classification of shot scale into Close-, Medium-, or Long-shots. The development of such a tool allows for investigating the relationship between shot scale computed of large movie corpora and the viewers' emotional involvement, for purposes such as movie recommendation, stylistic analysis, and film therapy, to name a few. Training and testing are performed on the full filmographies by six different authors (Scorsese, Godard, Tarr, Fellini, Antonioni, and Bergman) for a total number of 120 movies analysed second by second. Classification results are widely superior to state-of-the-art, with an overall accuracy around 94%. |
Year | Venue | Keywords |
|---|---|---|
2018 | 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | Shot scale, Movies, CNN |
Field | DocType | ISSN |
Scale analysis (statistics),Computer vision,Task analysis,Convolutional neural network,Computer science,Narrative,Angular distance,Natural language processing,Artificial intelligence | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mattia Savardi | 1 | 7 | 2.44 |
| A. Signoroni | 2 | 49 | 4.90 |
| Pierangelo Migliorati | 3 | 208 | 23.24 |
| Sergio Benini | 4 | 228 | 19.81 |