Mapping the Similarity Space of Paintings: Is There a Role for Image Statistics?

Dan Graham

Mathematics Department, Dartmouth College


It has been shown that basic image statistics are significantly different for paintings of various content and provenance. Though such statistics are crude for the purpose of classification, they may be useful for predicting perceptual judgments such as similarity or preference, since these statistics are related to efficient coding strategies in the visual system. To test this notion, we mapped the similarity space for digitized landscape paintings from a major university museum by collecting pairwise similarity ratings from observers. A multidimensional scaling analysis of observer responses showed strong contributions from basic image statistics relevant to the visual system, though semantic categories appear to play a larger role in determining perceived similarity. I will discuss our results and the prospects for using low level image statistics to perform high-level perceptual discriminations for artworks.

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