Automatic Prediction of Photographic Paper based on Photo Contest Information

Abstract

A photographic paper set an impression of a photograph. It strongly relates to expression of creative work. Nowadays, there are many types of photo paper available such as glossy paper and matte paper. These options provides creators with expressive capacity. On the other hand, it makes difficult to select the optimal paper harmonizing with a certain photo. From the reason, novice photographers often give up printing. For expert ones, it cost a lot of time to select a suitable paper by their own on applying to photographic contest. To solve such a problem, we aim to improve printing technique and provide creative support for both photographers. In this study, we built a system to estimate the optimal paper for a photo by machine learning techniques. The dataset were prepared from the photographic contest and the model were constructed with fine-tuning. Furthermore, we develop a function to visualize a part of photo which is contributed to estimate.