Improving Deep-Feature Image Similarity Calculation: A Case Study on an Ukiyo-e Card Matching Game Lottery

The purpose of this study is to improve AI players in Lottery. Lottery is a card matching game designed based on the concept of Audience Participation Game With a Purpose. Namely, it lets live streaming game audiences take part in gameplay and collects image similarity data perceived by these audiences. The game employs two AI players that initially calculate image similarities based on deep features (deep-feature similarities). In our previous study, it was found that similarities, between a certain pair of images, perceived by machines or computers – calculated based on deep features – were different from similarities perceived by humans. This would make gameplay by the AI players unbelievable, in other words, non-human-like. This study, therefore, proposes to use a linear model, built based on pre-collected human data, for improving the deep-feature similarities. The amount of human data required to make the model stable is also discussed. Experimental results show that the linear model only requires small quantities of human data to greatly improve the deep-feature similarities. At the same time, our results also show that the game Lottery is indispensable. This is because the linear model can only make the calculated similarity closer to that of humans, but there is still discernible difference; in order to obtain accurate similarity between images, it is necessary to collect a certain amount of human-perceived similarity data.