2026-09-19T01:27:07Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/430742022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Deb K. Roy.
Kubat, Rony Daniel
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2008-11-07T18:59:12Z
2008-11-07T18:59:12Z
2008
2008
http://hdl.handle.net/1721.1/43074
244251133
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.
Includes bibliographical references (p. 93-97).
Humans are sensitive to situational and semantic context when applying labels to colors. This is especially challenging for algorithms which attempt to replicate human categorization for communicative tasks. Additionally, mismatched color models between dialog partners can lead to a back-and-forth negotiation of terms to find common ground. This thesis presents a color-classification algorithm that takes advantage of a dialog-like interaction model to provide fast-adaptation for a specific exchange. The model learned in each exchange is then integrated into the system as a whole. This algorithm is an incremental meta-learner, leveraging a generic online-learner and adding context-sensitivity. A human study is presented, assessing the extent of semantic contextual effects on color naming. An evaluation of the algorithm based on the corpus gathered in this experiment is then tendered.
by Rony Daniel Kubat.
S.M.
97 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
A context-sensitive meta-classifier for color-naming
Thesis
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