Detection of Features Affording a Certain Action via Analysis of CNN

Abstract

Ideal products offer proper usages to users intuitively, and a usage perceived by a user is called an affordance. We aim to identify product features that induce the affordance of a specific action. We propose a method that identifies those affordance features without the need of an expert's knowledge of a domain. Using a dataset of a product's image and an affordance perceived by the product's user, the proposed method identifies those affordance features. The proposed method consists of three steps. First, we train a convolutional neural network (CNN) to predict a product's affordance. Second, according to the analysis of a trained CNN, we enumerate candidates for affordance features. Third, we use three metrics to verify and evaluate the candidates for features. By taking an affordance of “sit” as an example, our experiment showed that the proposed method does successfully identify affordance features.