The Pictures by Category and Similarity (PiCS) database: A multidimensional scaling database of 1200 images across 20 categories

Visual similarity is an essential concept in vision science, and the methods used to quantify similarity have recently expanded in the areas of human-derived ratings and computer vision methodologies. Researchers who want to manipulate similarity between images (e.g., in a visual search, categorization, or memory task) often use the aforementioned methods, which require substantial, additional data collection prior to the primary task of interest. To alleviate this problem, we have developed an openly available database that uses multidimensional scaling (MDS) to model the similarity among 1200 items spread across 20 object categories, thereby allowing researchers to utilize similarity ratings within and between categories. In this article, we document the development of this database, including (1) collecting similarity ratings using the spatial arrangement method across two sites, (2) our computational approach with MDS, and (3) validation of the MDS space by comparing SpAM-derived distances to direct similarity ratings. The database and similarity data provided between items (and across categories) will be useful to researchers wanting to manipulate or control similarity in their studies.