Unsupervised learning reveals interpretable latent representations for translucency perception
Fig 7
Visualization of features for translucency.
(A) The intermediate generative results (tRGB layer output at 64 pixels × 64 pixels resolution) of the images from the high-translucency dataset. The images are resized for display. (B) Middle-layer ICA kernels obtained by training a system of 64 basis functions on 24 pixels × 24 pixels image patches extracted from images in (A). The kernels are of size 24 × 24. (C) Visualization of applying three-dimensional convolution of the individual ICA kernels in (B) on a real photograph of translucent soap. (D) The resulting filtered images of four different soaps with selected chromatic kernels. The mid-to-low spatial frequency chromatic kernels can capture features of translucency such as “chromatic caustics” (row 2, column 1), “glowing edges” (row 1, column 4), and “inner glow” (row 1, column 1 and 4). The orientation-free kernel in the last row reveals the variation of color over a relatively coarse spatial scale, which is also diagnostic of translucency.