Literature DB >> 23847302

Perceptual qualities and material classes.

Roland W Fleming1, Christiane Wiebel, Karl Gegenfurtner.   

Abstract

Under typical viewing conditions, we can easily group materials into distinct classes (e.g., woods, plastics, textiles). Additionally, we can also make many other judgments about material properties (e.g., hardness, rigidity, colorfulness). Although these two types of judgment (classification and inferring material properties) have different requirements, they likely facilitate one another. We conducted two experiments to investigate the interactions between material classification and judgments of material qualities in both the visual and semantic domains. In Experiment 1, nine students viewed 130 images of materials from 10 different classes. For each image, they rated nine subjective properties (glossiness, transparency, colorfulness, roughness, hardness, coldness, fragility, naturalness, prettiness). In Experiment 2, 65 subjects were given the verbal names of six material classes, which they rated in terms of 42 adjectives describing material qualities. In both experiments, there was notable agreement between subjects, and a relatively small number of factors (weighted combinations of different qualities) were substantially independent of one another. Despite the difficulty of classifying materials from images (Liu, Sharan, Adelson, & Rosenholtz, 2010), the different classes were well clustered in the feature space defined by the subjective ratings. K-means clustering could correctly identify class membership for over 90% of the samples, based on the average ratings across subjects. We also found a high degree of consistency between the two tasks, suggesting subjects access similar information about materials whether judging their qualities visually or from memory. Together, these findings show that perceptual qualities are well defined, distinct, and systematically related to material class membership.

Entities:  

Keywords:  clustering; image classification; materials; object recognition; surface perception; texture perception

Mesh:

Year:  2013        PMID: 23847302     DOI: 10.1167/13.8.9

Source DB:  PubMed          Journal:  J Vis        ISSN: 1534-7362            Impact factor:   2.240


  16 in total

1.  Accuracy and speed of material categorization in real-world images.

Authors:  Lavanya Sharan; Ruth Rosenholtz; Edward H Adelson
Journal:  J Vis       Date:  2014-08-13       Impact factor: 2.240

2.  Soft like velvet and shiny like satin: Perceptual material signatures of fabrics depicted in 17th century paintings.

Authors:  Francesca Di Cicco; Mitchell J P van Zuijlen; Maarten W A Wijntjes; Sylvia C Pont
Journal:  J Vis       Date:  2021-05-03       Impact factor: 2.240

3.  Visual perception of procedural textures: identifying perceptual dimensions and predicting generation models.

Authors:  Jun Liu; Junyu Dong; Xiaoxu Cai; Lin Qi; Mike Chantler
Journal:  PLoS One       Date:  2015-06-24       Impact factor: 3.240

4.  Aesthetics by Numbers: Links between Perceived Texture Qualities and Computed Visual Texture Properties.

Authors:  Richard H A H Jacobs; Koen V Haak; Stefan Thumfart; Remco Renken; Brian Henson; Frans W Cornelissen
Journal:  Front Hum Neurosci       Date:  2016-07-21       Impact factor: 3.169

5.  Image Statistics and the Representation of Material Properties in the Visual Cortex.

Authors:  Elisabeth Baumgartner; Karl R Gegenfurtner
Journal:  Front Psychol       Date:  2016-08-17

6.  Sensory and Emotional Perception of Wooden Surfaces through Fingertip Touch.

Authors:  Shiv R Bhatta; Kaisa Tiippana; Katja Vahtikari; Mark Hughes; Marketta Kyttä
Journal:  Front Psychol       Date:  2017-03-13

7.  Dynamic Visual Cues for Differentiating Mirror and Glass.

Authors:  Hideki Tamura; Hiroshi Higashi; Shigeki Nakauchi
Journal:  Sci Rep       Date:  2018-05-30       Impact factor: 4.379

8.  Children's use of visual summary statistics for material categorization.

Authors:  Benjamin Balas
Journal:  J Vis       Date:  2017-10-01       Impact factor: 2.240

9.  Identifying shape transformations from photographs of real objects.

Authors:  Filipp Schmidt; Roland W Fleming
Journal:  PLoS One       Date:  2018-08-16       Impact factor: 3.240

10.  Human visual cortical responses to specular and matte motion flows.

Authors:  Tae-Eui Kam; Damien J Mannion; Seong-Whan Lee; Katja Doerschner; Daniel J Kersten
Journal:  Front Hum Neurosci       Date:  2015-10-21       Impact factor: 3.169

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