A context-sensitive meta-classifier for color-naming
Name
244251133-MIT.pdf
Description
Full printable version
Size
9.59 MB
Format
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Checksum (MD5)
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Author(s)
Kubat, Rony Daniel
Advisor(s)
Deb K. Roy.
Date Issued
2008
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.
Includes bibliographical references (p. 93-97).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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reproduction or distribution in any format is prohibited without written
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