An image-computable model of human visual shape similarity
Fig 2
The high-dimensionality of real-world shapes.
(A) t-SNE visualization of 2000 animal silhouettes arranged by their similarities according to a combination of 109 shape descriptors. Colour indicates basic level category. Insets highlight local structure: bloated shapes with tiny limbs (left); legged rectangular shapes (middle); small spiky shapes (right). To test whether human shape similarity is predicted in the high-dimensional animal space, we gathered human shape similarity judgments on horses (purple), rabbits (yellow), and other animals. (B) Human similarity arrangements of horse silhouettes, and (C) of silhouettes across multiple categories of animals (multidimensional scaling; dissimilarity: distances, criterion: metric stress). Similarity arrangement for (D) horse silhouettes and (E) multiple categories of animals in the full model based on 109 shape descriptors (multidimensional scaling; dissimilarity: distances, criterion: metric stress). Shapes with same colour across B and D or C and E are also the same. (F). Human arrangements correlate with the model for horse (purple), rabbit (yellow), and multiple animal silhouettes (gray) (r = 0.63, p < 0.01). (G). Across 25,712 animal shapes, 22 dimensions account for >95% of the variance (multidimensional scaling; dissimilarity: distances, criterion: metric stress). We call these 22 dimensions ShapeComp. (H) The space spanned by these ShapeComp dimensions regularly occurs across combinations of different animal sets (‘Animals’) and shape descriptors (‘Descriptors’). The pairwise distances across 200 test shapes are highly correlated across ShapeComp computed from 10 different sets of 500 randomly chosen animal shapes (‘Animals’), and also, but to a lesser degree, across 10 different sets of randomly selected shape descriptors (‘Descriptors’; 55 out of 109).