Could you guess an interesting movie from the posters?: An evaluation of vision-based features on movie poster database
This work addresses the challenge of modeling human taste for movie recommendations, but it is incremental as it applies existing feature extraction methods to a new dataset.
The paper tackled the problem of predicting film festival winners using only movie posters, without ratings or box-office data, by creating a new database of posters from four major festivals and testing various vision-based features. The results showed that color and facial emotion features performed well, with the Academy award estimation achieving better rates using color features.
In this paper, we aim to estimate the Winner of world-wide film festival from the exhibited movie poster. The task is an extremely challenging because the estimation must be done with only an exhibited movie poster, without any film ratings and box-office takings. In order to tackle this problem, we have created a new database which is consist of all movie posters included in the four biggest film festivals. The movie poster database (MPDB) contains historic movies over 80 years which are nominated a movie award at each year. We apply a couple of feature types, namely hand-craft, mid-level and deep feature to extract various information from a movie poster. Our experiments showed suggestive knowledge, for example, the Academy award estimation can be better rate with a color feature and a facial emotion feature generally performs good rate on the MPDB. The paper may suggest a possibility of modeling human taste for a movie recommendation.