In this paper, we propose a method for automatically estimating three typical human-impression factors, "hard-soft", "flashy-sober", and "stable-unstable" which are obtained from objects by analyzing their three-dimensional shapes. By realizing this method, a designer's will in directly shaping an object can be reflected during the design process. Here, the focus is highly correlating human impressions to the three-dimensional shape representation of objects. Previous work includes a method for estimating human impressions by using specially designed features and linear classifiers. However, it can be used for only the "hard-soft" impression factor because the feature has been optimized for this impression. The performance of this method is relatively low, and its processing time is low. In addition to, there is a serious problem in which this method can be used for only a particular impression factor. The purpose of this research is to propose a new method that can apply to all three typical impression factors mentioned above. First, we use a single RGB image that was acquired from a specific view direction instead of general three-dimensional mesh data from the range finder. This enables a very simple system consisting of a single camera. Second, we use a deep neural network as a nonlinear classifier. For our experiment, a lot of learning sample images with numerical human-impression factors were used. As for annotating correct impression factors as ground-truths, we utilized the SD (semantic differential) method, which is very popular in the field of psychological statistics. We have shown that the success rate of the proposed method is 83% for "hard-sofi", 78% for "flashy-sober", and 80% for "stable-unstable" when using test images that are not included in the learning data.
Koichi Taguchi, Manabu Hashimoto, Kensuke Tobitani, Noriko Nagata, "An Estimation Method of Human Impression Factors for Objects from their 3D Shapes Using a Deep Neural Network" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Processing: Algorithms and Systems XVI, 2018, pp 194-1 - 194-6, https://doi.org/10.2352/ISSN.2470-1173.2018.13.IPAS-194