Visual perception of texture regularity: Conjoint measurements and a wavelet response-distribution model

Abstract

Texture regularity, such as the repeating pattern in a carpet, brickwork or tree bark, is a ubiquitous feature of the visual world. The perception of regularity has generally been studied using multi-element textures in which the degree of regularity has been manipulated by adding random jitter to the elements’ positions. Here we used three-factor Maximum Likelihood Conjoint Measurement (MLCM) for the first time to investigate the encoding of regularity information under more complex conditions in which element spacing and size, in addition to positional jitter, were manipulated. Human observers were presented with large numbers of pairs of multi-element stimuli with varying levels of the three factors, and indicated on each trial which stimulus appeared more regular. All three factors contributed to regularity perception. Jitter, as expected, strongly affected regularity perception. This effect of jitter on regularity perception is strongest at small element spacing and large texture element size, suggesting that the visual system utilizes the edge-to-edge distance between elements as the basis for regularity judgments. We then examined how the responses of a bank of Gabor wavelet spatial filters might account for our results. Our analysis indicates that the peakedness of the spatial frequency (SF) distribution, a previously favored proposal, is insufficient for regularity encoding since it varied more with element spacing and size than with jitter. Instead, our results support the idea that the visual system may extract texture regularity information from the moments of the SF-distribution across orientation. In our best-performing model, the variance of SF-distribution skew across orientations can explain 70% of the variance of estimated texture regularity from our data, suggesting that it could provide a candidate read-out for perceived regularity.

Conjoint measurement of perceived transparency and perceived contrast in variegated checkerboards

“…The rapid increase in number of trials for this type of scaling procedure (Maximum Likelihood Difference Scaling (MLDS), MLCM) renders the procedures less attractive from a pragmatic point of view and might sometimes outweigh the theoretical benefits discussed above. Alternative strategies such as subsampling ( Knoblauch & Maloney, 2012 ; Abbatecola et al, 2021 ), having a reduced number of stimuli per dimension ( Sun et al, 2021 ), or the use of so-called embedded methods from the machine learning community (see Haghiri et al, 2020 , for example) are currently being explored and might allow more efficient ways of perceptual scale measurements in the future.…”

Color appearance of spatial patterns compared by direct estimation and conjoint measurement

“…The column labels identify the observers; average functions are presented in the rightmost panels. The abscissa cone contrast levels of the uniform square were scaled to take into account that the space-averaged cone contrast levels of the red-gray checkerboard are one-half those of the uniform square of the same cone contrast levels [10]. The observers were ordered with respect to their estimations at the maximum contrast, and then their identifications were assigned so that Observer 1 gave the highest ratings and Observer 5 the lowest.…”

“…Here, we compare DE and Maximum Likelihood Conjoint Measurement (MLCM) in a replication of experiments that investigated the influence of spatial complexity and cone contrast on color appearance. MLCM is based on paired-comparisons and is used to estimate perceptual scales associated with the integration of information along multiple dimensions [8][9][10].…”

Deep convolutional neural networks for regular texture recognition

“…In Park et al (2009) a Markov Random Field (MRF) with a Mean-Shift Belief Propagation method was used. Other approaches to texton grouping include optimization of shape alignment ( Cai & Baciu, 2011 ), structural regularity using symmetry groups ( Liu et al, 2008 ), projection profiles ( Aksoy, Yalniz & Tasdemir, 2012 ) and frequency filtering ( Hettiarachchi, Peters & Bruce, 2014 ; Sun et al, 2021 ).…”