# Could you guess an interesting movie from the posters?: An evaluation of vision-based features on movie poster database

## TL;DR

This paper evaluates vision-based features extracted from a new movie poster database to predict award winners, demonstrating that color and facial emotion features can improve estimation accuracy and hinting at modeling human taste for recommendations.

## Contribution

Introduces a new comprehensive movie poster database and assesses various vision-based features for predicting film award winners.

## Key findings

- Color features improve award prediction accuracy.
- Facial emotion features perform well in estimation.
- Possibility of modeling human taste for movie recommendation.

## Abstract

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.