Visual and Semantic Similarity in ImageNet
Venue
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2011), pp. 1777-1784
Publication Year
2011
Authors
Thomas Deselaers, Vittorio Ferrari
BibTeX
Abstract
Many computer vision approaches take for granted positive answers to questions such
as “Are semantic categories visually separable?” and “Is visual similarity
correlated to semantic similarity?” In this paper, we study experimentally whether
these assumptions hold and show parallels to questions investigated in cognitive
science about the human visual system. The insights gained from our analysis enable
building a novel distance function between images assessing whether they are from
the same basic-level category. This function goes beyond direct visual distance as
it also exploits semantic similarity measured through ImageNet. We demonstrate
experimentally that it outperforms purely visual distances.
