Inferring Image Templates from Classification Decisions
Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence. Pages 1446-1450.
Abstract
Assuming human image classification decisions are based on estimating the degree of match between a small number of stored internal templates and certain regions of the input images, we present an algorithm which infers observers classification templates from their classification decisions on a set of test images. The problem is formulated as learning prototypes from labeled data under an adjustable, prototype-specific elliptical metric. The matrix of the elliptical metric indicates the pixels that the template responds to. The model was applied to human psychophysical data collected in a simple image classification experiment.
@inproceedings{ijcai2005p183,
title = {Inferring Image Templates from Classification Decisions},
author = {Dhua, Arnab and Cutzu, Florin},
booktitle = {Proceedings of the Nineteenth International Joint Conference on
Artificial Intelligence, {IJCAI-05}},
publisher = {International Joint Conferences on Artificial Intelligence},
pages = {1446--1450},
year = {2005},
url = {https://ijcai.org/proceedings/2005/183},
}
TY - CPAPER TI - Inferring Image Templates from Classification Decisions AU - Dhua, Arnab AU - Cutzu, Florin T2 - Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05 PB - International Joint Conferences on Artificial Intelligence PY - 2005 SP - 1446 EP - 1450 UR - https://ijcai.org/proceedings/2005/183 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: