Some Objects Are More Equal than Others: Measuring and Predicting Importance
Abstract
We observe that everyday images contain dozens of objects, and that humans, in describing these images, give different priority to these objects. We argue that a goal of visual recognition is, therefore, not only to detect and classify objects but also to associate with each a level of priority which we call ‘importance’. We propose a definition of importance and show how this may be estimated reliably from data harvested from human observers. We conclude by showing that a first-order estimate of importance may be computed from a number of simple image region measurements and does not require access to image meaning.
Cite
Text
Spain and Perona. "Some Objects Are More Equal than Others: Measuring and Predicting Importance." European Conference on Computer Vision, 2008. doi:10.1007/978-3-540-88682-2_40Markdown
[Spain and Perona. "Some Objects Are More Equal than Others: Measuring and Predicting Importance." European Conference on Computer Vision, 2008.](https://mlanthology.org/eccv/2008/spain2008eccv-some/) doi:10.1007/978-3-540-88682-2_40BibTeX
@inproceedings{spain2008eccv-some,
title = {{Some Objects Are More Equal than Others: Measuring and Predicting Importance}},
author = {Spain, Merrielle and Perona, Pietro},
booktitle = {European Conference on Computer Vision},
year = {2008},
pages = {523-536},
doi = {10.1007/978-3-540-88682-2_40},
url = {https://mlanthology.org/eccv/2008/spain2008eccv-some/}
}