Prediction of Search Targets From Fixations in Open-world Settings

Hosnieh Sattar, Sabine Müller, Mario Fritz, Andreas Bulling

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 981–990, 2015.

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.

Links

doi: 10.1109/CVPR.2015.7298700

BibTeX

@inproceedings{sattar15_cvpr, title = {Prediction of Search Targets From Fixations in Open-world Settings}, author = {Sattar, Hosnieh and M{\"{u}}ller, Sabine and Fritz, Mario and Bulling, Andreas}, year = {2015}, booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, pages = {981--990}, doi = {10.1109/CVPR.2015.7298700} }