Combining metrics for mesh simplification and parameterization
Jordan Smith, Ioana Boier-Martin
SIGGRAPH 2005
Language is often thought as being poorly adapted to precisely describe or quantify smell and olfactory attributes. In this work, we show that semantic descriptors of odors can be implemented in a model to successfully predict odor mixture discriminability, an olfactory attribute. We achieved this by taking advantage of the structure-to-percept model we previously developed for monomolecular odorants, using chemical descriptors to predict pleasantness, intensity and 19 semantic descriptors such as ‘fish’, ‘cold’, ‘burnt’, ‘garlic’, ‘grass’ and ‘sweet’ for odor mixtures, followed by a metric learning to obtain odor mixture discriminability. Through this expansion of the representation of olfactory mixtures, our Semantic model outperforms state of the art methods by taking advantage of the intermediary semantic representations learned from human perception data to enhance and generalize the odor discriminability/similarity predictions. As 10 of the semantic descriptors were selected to predict discriminability/similarity, our approach meets the need of rapidly obtaining interpretable attributes of odor mixtures as illustrated by the difficulty of finding olfactory metamers. More fundamentally, it also shows that language can be used to establish a metric of discriminability in the everyday olfactory space.
Jordan Smith, Ioana Boier-Martin
SIGGRAPH 2005
Michael Muller, Heloisa Caroline de Souza Pereira Candello, et al.
ICCC 2023
Sören Bleikertz, Carsten Vogel, et al.
ACSAC 2014
Bc Kwon, Natasha Mulligan, et al.
ISMB 2025