Inferring Image Templates from Classification Decisions
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.
Cite
Text
Dhua and Cutzu. "Inferring Image Templates from Classification Decisions." International Joint Conference on Artificial Intelligence, 2005.Markdown
[Dhua and Cutzu. "Inferring Image Templates from Classification Decisions." International Joint Conference on Artificial Intelligence, 2005.](https://mlanthology.org/ijcai/2005/dhua2005ijcai-inferring/)BibTeX
@inproceedings{dhua2005ijcai-inferring,
title = {{Inferring Image Templates from Classification Decisions}},
author = {Dhua, Arnab and Cutzu, Florin},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2005},
pages = {1446-1451},
url = {https://mlanthology.org/ijcai/2005/dhua2005ijcai-inferring/}
}