Can machine learning account for human visual object shape similarity judgments?
Vision Res
; 167: 87-99, 2020 02.
Article
en En
| MEDLINE
| ID: mdl-31972448
ABSTRACT
We describe and analyze the performance of metric learning systems, including deep neural networks (DNNs), on a new dataset of human visual object shape similarity judgments of naturalistic, part-based objects known as "Fribbles". In contrast to previous studies which asked participants to judge similarity when objects or scenes were rendered from a single viewpoint, we rendered Fribbles from multiple viewpoints and asked participants to judge shape similarity in a viewpoint-invariant manner. Metrics trained using pixel-based or DNN-based representations fail to explain our experimental data, but a metric trained with a viewpoint-invariant, part-based representation produces a good fit. We also find that although neural networks can learn to extract the part-based representation-and therefore should be capable of learning to model our data-networks trained with a "triplet loss" function based on similarity judgments do not perform well. We analyze this failure, providing a mathematical description of the relationship between the metric learning objective function and the triplet loss function. The poor performance of neural networks appears to be due to the nonconvexity of the optimization problem in network weight space. We conclude that viewpoint insensitivity is a critical aspect of human visual shape perception, and that neural network and other machine learning methods will need to learn viewpoint-insensitive representations in order to account for people's visual object shape similarity judgments.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Reconocimiento Visual de Modelos
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Aprendizaje Automático
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Percepción de Forma
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Juicio
Límite:
Humans
Idioma:
En
Revista:
Vision Res
Año:
2020
Tipo del documento:
Article