Metadata
Title
A Probabilistic Model of the Categorical Association between Colors
Category
general
UUID
2b79438ebdf14298b3d7634abb28dbfd
Source URL
https://idl.uw.edu/papers/color-names
Parent URL
https://idl.uw.edu/papers
Crawl Time
2026-03-11T03:10:08+00:00
Rendered Raw Markdown

A Probabilistic Model of the Categorical Association between Colors

Source: https://idl.uw.edu/papers/color-names Parent: https://idl.uw.edu/papers

Jason Chuang, Maureen Stone, Pat Hanrahan. Proc. Color Imaging Conference, 2008

Jason Chuang, Maureen Stone, Pat Hanrahan

Proc. Color Imaging Conference, 2008

Materials

PDF | Presentation

Abstract

In this paper we describe a non-parametric probabilistic model that can be used to encode relationships in color naming datasets. This model can be used with datasets with any number of color terms and expressions, as well as terms from multiple languages. Because the model is based on probability theory, we can use classic statistics to compute features of interest to color scientists. In particular, we show that the uniqueness of a color name (color saliency) can be captured using the entropy of the probability distribution. We demonstrate this approach by applying this model to two different datasets: the multi-lingual World Color Survey (WCS), and a database collected via the web by Dolores Labs. We demonstrate how saliency clusters similarly named colors for both datasets, and compare our WCS results to those of Kay and his colleagues. We compare the two datasets to each other by converting them to a common colorspace (IPT).

BibTeX

@inproceedings{2008-color-names,
  title = {A Probabilistic Model of the Categorical Association between Colors},
  author = {Chuang, Jason AND Stone, Maureen AND Hanrahan, Pat},
  booktitle = {Proc. Color Imaging Conference},
  year = {2008},
  pages = {6--11},
  url = {https://idl.uw.edu/papers/color-names},
  doi = {10.2352/CIC.2008.16.1.art00002}
}

{"status":200,"statusText":"","headers":{},"body":"[\n {\n \"fullName\": \"Proc. ACM Human Factors in Computing Systems (CHI)\",\n \"nickname\": \"CHI\",\n \"venueType\": \"conference\"\n },\n {\n \"fullName\": \"IEEE Trans. Visualization & Comp. Graphics (Proc. VIS)\",\n \"nickname\": \"VIS\",\n \"venueType\": \"journal\"\n },\n {\n \"fullName\": \"Computer Graphics Forum (Proc. EuroVis)\",\n \"nickname\": \"EuroVis\",\n \"venueType\": \"journal\"\n },\n {\n \"fullName\": \"Proc. EuroVis Short Papers\",\n \"nickname\": \"EuroVis-Short\",\n \"venueType\": \"conference\"\n },\n {\n \"fullName\": \"Proc. IEEE VIS Short Papers\",\n \"nickname\": \"VIS-Short\",\n \"venueType\": \"conference\"\n },\n {\n \"fullName\": \"Proc. ACM User Interface Software & Technology (UIST)\",\n \"nickname\": \"UIST\",\n \"venueType\": \"conference\"\n },\n {\n \"fullName\": \"Proc. ACM Computer-Supported Cooperative Work (CSCW)\",\n \"nickname\": \"CSCW\",\n \"venueType\": \"conference\"\n },\n {\n \"fullName\": \"Proc. ACM Intelligent User Interfaces\",\n \"nickname\": \"IUI\",\n \"venueType\": \"conference\"\n },\n {\n \"fullName\": \"ACM Trans. on Computer-Human Interaction\",\n \"nickname\": \"ACM TOCHI\",\n \"venueType\": \"journal\"\n },\n {\n \"fullName\": \"Proc. Advanced Visual Interfaces\",\n \"nickname\": \"AVI\",\n \"venueType\": \"conference\"\n },\n {\n \"fullName\": \"Proc. Conference on Innovative Data Systems Research (CIDR)\",\n \"nickname\": \"CIDR\",\n \"venueType\": \"conference\"\n },\n {\n \"fullName\": \"Proc. Very Large Database Endowment (PVLDB)\",\n \"nickname\": \"PVLDB\",\n \"venueType\": \"journal\"\n },\n {\n \"fullName\": \"Proc. Empirical Methods in Natural Language Processing\",\n \"nickname\": \"EMNLP\",\n \"venueType\": \"conference\"\n },\n {\n \"fullName\": \"Proc. NAACL-HLT\",\n \"nickname\": \"NAACL-HLT\",\n \"venueType\": \"conference\"\n },\n {\n \"fullName\": \"Proc. International Conference on Weblogs and Social Media (ICWSM)\",\n \"nickname\": \"ICWSM\",\n \"venueType\": \"conference\"\n },\n {\n \"fullName\": \"IEEE Trans. Visualization & Comp. Graphics (Proc. InfoVis)\",\n \"nickname\": \"InfoVis\",\n \"venueType\": \"journal\"\n },\n {\n \"fullName\": \"Beautiful Data\",\n \"nickname\": \"Beautiful Data\",\n \"venueType\": \"book\"\n },\n {\n \"fullName\": \"Information Visualization Journal\",\n \"nickname\": \"IV Journal\",\n \"venueType\": \"journal\"\n },\n {\n \"fullName\": \"Proc. IEEE Visual Analytics Science & Technology (VAST)\",\n \"nickname\": \"VAST\",\n \"venueType\": \"conference\"\n },\n {\n \"fullName\": \"Cortex\",\n \"nickname\": \"Cortex\",\n \"venueType\": \"journal\"\n },\n {\n \"fullName\": \"Proc. Hawaii International Conference on Systems Sciences (HICSS)\",\n \"nickname\": \"HICSS\",\n \"venueType\": \"conference\"\n },\n {\n \"fullName\": \"Proc. IEEE Information Visualization (InfoVis)\",\n \"nickname\": \"InfoVis (Pre-TVCG)\",\n \"venueType\": \"conference\"\n },\n {\n \"fullName\": \"Proc. Ubiquitous Computing\",\n \"nickname\": \"UbiComp\",\n \"venueType\": \"conference\"\n },\n {\n \"fullName\": \"Proc. WEBKDD Workshop\",\n \"nickname\": \"WEBKDD\",\n \"venueType\": \"workshop\"\n },\n {\n \"fullName\": \"ACM Trans. on Information Systems\",\n \"nickname\": \"ACM TOIS\",\n \"venueType\": \"journal\"\n },\n {\n \"fullName\": \"Communications of the ACM\",\n \"nickname\": \"CACM\",\n \"venueType\": \"journal\"\n },\n {\n \"fullName\": \"Proc. Workshop on Social Network Mining & Analysis, ACM KDD\",\n \"nickname\": \"SNAKDD\",\n \"venueType\": \"workshop\"\n },\n {\n \"fullName\": \"Proc. Social Visualization Workshop, ACM CHI\",\n \"nickname\": \"CHI Social Vis\",\n \"venueType\": \"workshop\"\n },\n {\n \"fullName\": \"Proc. AVI Workshop on Invisible & Transparent Interfaces\",\n \"nickname\": \"AVI ITI\",\n \"venueType\": \"workshop\"\n },\n {\n \"fullName\": \"Proc. Color Imaging Conference\",\n \"nickname\": \"Color Imaging Conf.\",\n \"venueType\": \"conference\"\n },\n {\n \"fullName\": \"Proc. Workshop on Applications for Topic Models, NIPS\",\n \"nickname\": \"NIPS Topic Model Ws\",\n \"venueType\": \"workshop\"\n },\n {\n \"fullName\": \"Proc. Mining Software Repositories\",\n \"nickname\": \"MSR\",\n \"venueType\": \"conference\"\n },\n {\n \"fullName\": \"Journal of Animal Ecology\",\n \"nickname\": \"J Anim Eco\",\n \"venueType\": \"journal\"\n },\n {\n \"fullName\": \"J Am Med Inform Assoc\",\n \"nickname\": \"JAMIA\",\n \"venueType\": \"journal\"\n },\n {\n \"fullName\": \"Proc. International Conference on Machine Learning (ICML)\",\n \"nickname\": \"ICML\",\n \"venueType\": \"conference\"\n },\n {\n \"fullName\": \"Computer Graphics and Applications\",\n \"nickname\": \"CG&A\",\n \"venueType\": \"journal\"\n },\n {\n \"fullName\": \"Proc. IEEE Biological Data Visualization (BioVis)\",\n \"nickname\": \"BioVis\",\n \"venueType\": \"conference\"\n },\n {\n \"fullName\": \"Poetics\",\n \"nickname\": \"Poetics\",\n \"venueType\": \"journal\"\n },\n {\n \"fullName\": \"Proc. ACM Web Search and Data Mining (WSDM)\",\n \"nickname\": \"WSDM\",\n \"venueType\": \"conference\"\n },\n {\n \"fullName\": \"Proc. User Modeling and User-Adapted Interaction (UMUAI)\",\n \"nickname\": \"UMUAI\",\n \"venueType\": \"journal\"\n },\n {\n \"fullName\": \"Proc. Workshop on Eye Tracking and Visualization (ETVIS)\",\n \"nickname\": \"ETVIS\",\n \"venueType\": \"workshop\"\n },\n {\n \"fullName\": \"Trends in Ecology & Evolution\",\n \"nickname\": \"TREE\",\n \"venueType\": \"journal\"\n },\n {\n \"fullName\": \"PLOS ONE\",\n \"nickname\": \"PLOS ONE\",\n \"venueType\": \"journal\"\n },\n {\n \"fullName\": \"Proc. ACM SIGMOD Human-in-the-Loop Data Analysis (HILDA)\",\n \"nickname\": \"HILDA\",\n \"venueType\": \"workshop\"\n },\n {\n \"fullName\": \"IEEE Trans. Visualization & Comp. Graphics (Proc. VAST)\",\n \"nickname\": \"VAST-TVCG\",\n \"venueType\": \"journal\"\n },\n {\n \"fullName\": \"Proc. Workshop on Dealing with Cognitive Biases in Visualisations (DECISIVe), IEEE VIS\",\n \"nickname\": \"DECISIVe\",\n \"venueType\": \"workshop\"\n },\n {\n \"fullName\": \"arXiv\",\n \"nickname\": \"arXiv\",\n \"venueType\": \"journal\"\n },\n {\n \"fullName\": \"The Journal of Open Source Software\",\n \"nickname\": \"JOSS\",\n \"venueType\": \"journal\"\n },\n {\n \"fullName\": \"Proceedings of the National Academy of Sciences\",\n \"nickname\": \"PNAS\",\n \"venueType\": \"journal\"\n },\n {\n \"fullName\": \"Proc. Association for Computational Linguistics (ACL)\",\n \"nickname\": \"ACL\",\n \"venueType\": \"conference\"\n },\n {\n \"fullName\": \"Distill\",\n \"nickname\": \"Distill\",\n \"venueType\": \"journal\"\n },\n {\n \"fullName\": \"Harvard Data Science Review\",\n \"nickname\": \"HDSR\",\n \"venueType\": \"journal\"\n },\n {\n \"fullName\": \"Organizational Behavior and Human Decision Processes\",\n \"nickname\": \"OBHDP\",\n \"venueType\": \"journal\"\n },\n {\n \"fullName\": \"EPJ Data Science\",\n \"nickname\": \"EPJ-DS\",\n \"venueType\": \"journal\"\n },\n {\n \"fullName\": \"Proc. IEEE Symposium on Visual Languages and Human Centric Computing (VL/HCC)\",\n \"nickname\": \"VL/HCC\",\n \"venueType\": \"conference\"\n },\n {\n \"fullName\": \"Proc. ACM Management of Data (SIGMOD)\",\n \"nickname\": \"SIGMOD\",\n \"venueType\": \"conference\"\n },\n {\n \"fullName\": \"Companion of ACM Management of Data (SIGMOD)\",\n \"nickname\": \"SIGMOD-Demo\",\n \"venueType\": \"conference\"\n },\n {\n \"fullName\": \"IEEE Trans. Visualization & Comp. Graphics\",\n \"nickname\": \"TVCG\",\n \"venueType\": \"journal\"\n },\n {\n \"fullName\": \"Proc. ACM Creativity & Cognition\",\n \"nickname\": \"C&C\",\n \"venueType\": \"conference\"\n },\n {\n \"fullName\": \"Workshop on Intelligent and Interactive Writing Assistants (In2Writing)\",\n \"nickname\": \"In2Writing\",\n \"venueType\": \"workshop\"\n }\n]\n"} {"status":200,"statusText":"","headers":{},"body":"{\n \"title\": \"A Probabilistic Model of the Categorical Association between Colors\",\n \"year\": 2008,\n \"start_page\": 6,\n \"end_page\": 11,\n \"volume\": null,\n \"issue\": null,\n \"editors\": \"\",\n \"publisher\": \"\",\n \"location\": \"\",\n \"pdf\": \"https://idl.cs.washington.edu/files/2008-ColorNames-CIC.pdf\",\n \"abstract\": \"In this paper we describe a non-parametric probabilistic model that can be used to encode relationships in color naming datasets. This model can be used with datasets with any number of color terms and expressions, as well as terms from multiple languages. Because the model is based on probability theory, we can use classic statistics to compute features of interest to color scientists. In particular, we show that the uniqueness of a color name (color saliency) can be captured using the entropy of the probability distribution. We demonstrate this approach by applying this model to two different datasets: the multi-lingual World Color Survey (WCS), and a database collected via the web by Dolores Labs. We demonstrate how saliency clusters similarly named colors for both datasets, and compare our WCS results to those of Kay and his colleagues. We compare the two datasets to each other by converting them to a common colorspace (IPT).\",\n \"thumbnail\": \"images/thumbs/color-names.png\",\n \"figure\": \"\",\n \"caption\": \"\",\n \"web_name\": \"color-names\",\n \"visible\": true,\n \"mod_date\": \"2010-08-23\",\n \"note\": \"\",\n \"pub_date\": \"2008-11-15\",\n \"venue\": \"Color Imaging Conf.\",\n \"authors\": [\n {\n \"first_name\": \"Jason\",\n \"last_name\": \"Chuang\",\n \"url\": \"http://jason.chuang.info\"\n },\n {\n \"first_name\": \"Maureen\",\n \"last_name\": \"Stone\",\n \"url\": \"https://mcstone.github.io/\"\n },\n {\n \"first_name\": \"Pat\",\n \"last_name\": \"Hanrahan\",\n \"url\": \"http://graphics.stanford.edu/~hanrahan\"\n }\n ],\n \"materials\": [\n {\n \"name\": \"Presentation\",\n \"link\": \"http://idl.cs.washington.edu/files/2008-ColorNames-CIC-Talk.pdf\"\n }\n ],\n \"tags\": [],\n \"doi\": \"10.2352/CIC.2008.16.1.art00002\"\n}"}

{ __sveltekit_17copn9 = { base: new URL("..", location).pathname.slice(0, -1), assets: "/uwdata.github.io" }; const element = document.currentScript.parentElement; const data = [null,null]; Promise.all([ import("../_app/immutable/entry/start.CZdZnu7S.js"), import("../_app/immutable/entry/app.qRA-U4ZQ.js") ]).then(([kit, app]) => { kit.start(app, element, { node_ids: [0, 7], data, form: null, error: null }); }); }