PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Visual and Semantic Similarity in ImageNet
Thomas Deselaers and Vittorio Ferrari
In: CVPR 2011, 20-25 Jun 2011, Colorado Springs, CO, USA.

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.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:8143
Deposited By:Thomas Deselaers
Deposited On:04 May 2011