Measuring Shape Unexpectedness of Exhibits Based on Similarity and Outlier Detection

Maho Kinoshita, Wakana Kuwata, Hiroaki Ohshima

Abstract: This study proposes a method for finding museum exhibits that visually unexpected shapes, aiming to enhance visitor engagement and memory in museum. The proposed method measures shape-based unexpectedness by combining shape similarity computation with outlier detection. An exhibit is considered unexpected if it is identified as a shape-based outlier. To compute shape similarity, images are first converted into feature vectors using either Vision Transformer (ViT) or Convolutional Neural Networks (CNN). The study also investigates how converting color images into monochrome or line drawings affects the measurement of shape unexpectedness. For outlier detection, two methods, DBSCAN and PageRank-based approach are evaluated. Experiments were conducted using images of exhibits from the National Museum of Ethnology, Japan. Among all tested combinations, the pairing of ConvNeXt, a type of CNN, and PageRank-based approach achieved the highest performance with an nDCG@3 of 0.794.

External IDs:dblp:conf/iiwas/KinoshitaKO25