A context-sensitive meta-classifier for color-naming

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2008

Author dc:creator

• Kubat, Rony Daniel

Advisor dc:contributor.advisor

• Deb K. Roy.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/43074

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/43074