%0 Conference Paper %1 sattar15_arxiv %A Sattar, Hosnieh %A Müller, Sabine %A Fritz, Mario %A Bulling, Andreas %B Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) %D 2015 %K 2015 A07 sfbtrr161 visus:bullinas %P 981-990 %R 10.1109/CVPR.2015.7298700 %T Prediction of Search Targets From Fixations in Open-world Settings %U https://ieeexplore.ieee.org/document/7298700 %X Previous work on predicting the target of visual search from human fixations only considered closed-world settings in which training labels are available and predictions are performed for a known set of potential targets. In this work we go beyond the state of the art by studying search target prediction in an open-world setting in which we no longer assume that we have fixation data to train for the search targets. We present a dataset containing fixation data of 18 users searching for natural images from three image categories within synthesised image collages of about 80 images. In a closed-world baseline experiment we show that we can predict the correct target image out of a candidate set of five images. We then present a new problem formulation for search target prediction in the open-world setting that is based on learning compatibilities between fixations and potential targets.

@inproceedings{sattar15_arxiv, abstract = {Previous work on predicting the target of visual search from human fixations only considered closed-world settings in which training labels are available and predictions are performed for a known set of potential targets. In this work we go beyond the state of the art by studying search target prediction in an open-world setting in which we no longer assume that we have fixation data to train for the search targets. We present a dataset containing fixation data of 18 users searching for natural images from three image categories within synthesised image collages of about 80 images. In a closed-world baseline experiment we show that we can predict the correct target image out of a candidate set of five images. We then present a new problem formulation for search target prediction in the open-world setting that is based on learning compatibilities between fixations and potential targets.}, added-at = {2020-02-28T13:19:22.000+0100}, author = {Sattar, Hosnieh and M{\"{u}}ller, Sabine and Fritz, Mario and Bulling, Andreas}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2b5b10e5fdcd4472d702574199c246846/leonkokkoliadis}, booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, doi = {10.1109/CVPR.2015.7298700}, interhash = {f3ee02d65d5a228c9035164fe93bf796}, intrahash = {b5b10e5fdcd4472d702574199c246846}, keywords = {2015 A07 sfbtrr161 visus:bullinas}, pages = {981-990}, timestamp = {2020-02-28T12:19:22.000+0100}, title = {Prediction of Search Targets From Fixations in Open-world Settings}, url = {https://ieeexplore.ieee.org/document/7298700}, year = 2015 }