Eye tracking in web search tasks: design implications
ETRA '02 Proceedings of the 2002 symposium on Eye tracking research & applications
Exploring Human Eye Behaviour using a Model of Visual Attention
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Visual recognition: computational models and human psychophysics
Visual recognition: computational models and human psychophysics
Click Passwords Under Investigation
ESORICS '07 Proceedings of the 12th European symposium on Research In Computer Security
Can relevance of images be inferred from eye movements?
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Scale-invariant visual language modeling for object categorization
IEEE Transactions on Multimedia - Special issue on integration of context and content
Fast query point movement techniques with relevance feedback for content-based image retrieval
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Perceptual image retrieval using eye movements
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
Which Components are Important for Interactive Image Searching?
IEEE Transactions on Circuits and Systems for Video Technology
An eye-tracking-based approach to facilitate interactive video search
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
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Relevance feedback (RF) mechanisms are widely adopted in Content-Based Image Retrieval (CBIR) systems to improve image retrieval performance. However, there exist some intrinsic problems: (1) the semantic gap between high-level concepts and low-level features and (2) the subjectivity of human perception of visual contents. The primary focus of this paper is to evaluate the possibility of inferring the relevance of images based on eye movement data. In total, 882 images from 101 categories are viewed by 10 subjects to test the usefulness of implicit RF, where the relevance of each image is known beforehand. A set of measures based on fixations are thoroughly evaluated which include fixation duration, fixation count, and the number of revisits. Finally, the paper proposes a decision tree to predict the user's input during the image searching tasks. The prediction precision of the decision tree is over 87%, which spreads light on a promising integration of natural eye movement into CBIR systems in the future.