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dc.contributor.authorSaraee, Elhamen_US
dc.contributor.authorJalal, Monaen_US
dc.contributor.authorBetke, Margriten_US
dc.date.accessioned2019-09-03T14:11:29Z
dc.date.available2019-09-03T14:11:29Z
dc.date.issued2018-10-03
dc.identifierhttps://arxiv.org/abs/1810.01771
dc.identifier.citationElham Saraee, Mona Jalal, Margrit Betke. 2018. "SAVOIAS: A Diverse, Multi-Category Visual Complexity Dataset." pp. ? - ? (10).
dc.identifier.urihttps://hdl.handle.net/2144/37603
dc.description.abstractVisual complexity identifies the level of intricacy and details in an image or the level of difficulty to describe the image. It is an important concept in a variety of areas such as cognitive psychology, computer vision and visualization, and advertisement. Yet, efforts to create large, downloadable image datasets with diverse content and unbiased groundtruthing are lacking. In this work, we introduce Savoias, a visual complexity dataset that compromises of more than 1,400 images from seven image categories relevant to the above research areas, namely Scenes, Advertisements, Visualization and infographics, Objects, Interior design, Art, and Suprematism. The images in each category portray diverse characteristics including various low-level and high-level features, objects, backgrounds, textures and patterns, text, and graphics. The ground truth for Savoias is obtained by crowdsourcing more than 37,000 pairwise comparisons of images using the forced-choice methodology and with more than 1,600 contributors. The resulting relative scores are then converted to absolute visual complexity scores using the Bradley-Terry method and matrix completion. When applying five state-of-the-art algorithms to analyze the visual complexity of the images in the Savoias dataset, we found that the scores obtained from these baseline tools only correlate well with crowdsourced labels for abstract patterns in the Suprematism category (Pearson correlation r=0.84). For the other categories, in particular, the objects and advertisement categories, low correlation coefficients were revealed (r=0.3 and 0.56, respectively). These findings suggest that (1) state-of-the-art approaches are mostly insufficient and (2) Savoias enables category-specific method development, which is likely to improve the impact of visual complexity analysis on specific application areas, including computer vision.en_US
dc.description.urihttps://arxiv.org/pdf/1810.01771.pdf
dc.description.urihttps://arxiv.org/pdf/1810.01771.pdf
dc.format.extent10 p.en_US
dc.language.isoen_US
dc.publisherarXiven_US
dc.titleSAVOIAS: a diverse, multi-category visual complexity dataseten_US
dc.typeOtheren_US
pubs.elements-sourcemanual-entryen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Computer Scienceen_US
dc.identifier.orcid0000-0002-4491-6868 (Betke, Margrit)
dc.description.oaversionFirst author draft
dc.identifier.mycv437311


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