An image-computable model of human visual shape similarity
Fig 9
Using ShapeComp to evaluate shape similarity in existing shape sets.
Even with novel shapes from, as an example, the (A) validated circular shape space set (human data; from [90]), (B) ShapeComp’s predictions show many similarities to humans. While ShapeComp’s arrangement is more compressed, ShapeComp correctly predicts (i) large gaps between shapes 1 and 15, and 1 and 2, (ii) the circular nature of the data set, (iii) subjective difference between 1 and 11 is smaller than between 14 and 8, yielding the elongated arrangement. (C) Correlation between ShapeComp and human similarity judgments for the distances between all possible (105 pairs) (r = 0.78, p <0.01). Given the noise uncertainty across observers–which is unknown for the circular shape set—ShapeComp appears to be a good model of human behaviour. Note, given that some shapes in the circular shape set (e.g., 5 or 6) have multiple minimum x-values, we used KerNet2 which is based on images to compute the ShapeComp solution.