Despite the popularity of photo editors used to improve image attractiveness and expressiveness on social media, many users have trouble making sense of color lter e ects and locating a preferred lter among a set of designer-crafted candidates. The problem gets worse when more computer-generated lters are introduced. To enhance lter ndability, we semantically name and organize color e ects leveraging data curated by creative communities online. We rst model semantic mappings between color themes and keywords in everyday language. Next, we index and organize each lter by the derived semantic information. We conduct three separate studies to investigate the bene t of the semantic features on lter exploration. Our results indicate that color theme semantics constructed through social curation enhance lter ndability, and provide important evidence with regard to using the wisdom of the crowd to improve user experience with image editors.

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@article{10.1145/3274456, author = {Wu, Ziming and Sun, Zhida and Kim, Taewook and Reani, Manuele and Jay, Caroline and Ma, Xiaojuan}, title = {Mediating Color Filter Exploration with Color Theme Semantics Derived from Social Curation Data}, year = {2018}, issue_date = {November 2018}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {2}, number = {CSCW}, url = {https://doi.org/10.1145/3274456}, doi = {10.1145/3274456}, journal = {Proc. ACM Hum.-Comput. Interact.}, month = nov, articleno = {Article 187}, numpages = {24}, keywords = {color filter, color theme semantics, data-driven design, social curation} }