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IEEE Transactions on Pattern Analysis and Machine Intelligence
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LabelMe: A Database and Web-Based Tool for Image Annotation
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Query expansion using gaze-based feedback on the subdocument level
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
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MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
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IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Automatic and continuous user task analysis via eye activity
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Locating user attention using eye tracking and EEG for spatio-temporal event selection
Proceedings of the 2013 international conference on Intelligent user interfaces
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Labeled image regions provide very valuable information that can be used in different settings such as image search. The manual creation of region labels is a tedious task. Fully automatic approaches lack understanding the image content sufficiently due to the huge variety of depicted objects. Our approach benefits from the expected spread of eye tracking hardware and uses gaze information obtained from users performing image search tasks to automatically label image regions. This allows to exploit the human capabilities regarding the visual perception of image content while performing daily routine tasks. In an experiment with 23 participants, we show that it is possible to assign search terms to photo regions by means of gaze analysis with an average precision of 0.56 and an average F-measure of 0.38 over 361 photos. The participants performed different search tasks while their gaze was recorded. The results of the experiment show that the gaze-based approach performs significantly better than a baseline approach based on saliency maps.