Inferring object relevance from gaze in dynamic scenes

Eye Tracking Research and Applications Symposium (ETRA)

Volumn , Issue , 2010, Pages 105-108

  Kandemir, Melih ^a   Saarinen, Veli Matti ^a   Kaski, Samuel ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

Augmented reality; Gaze tracking; Information retrieval; Intelligent user interfaces; Machine learning; Ordinal logistic regression

Indexed keywords

DATA AUGMENTATION; DYNAMIC SCENES; FEASIBILITY STUDIES; GAZE TRACKING; INTELLIGENT USER INTERFACES; MACHINE-LEARNING; ORDINAL LOGISTIC REGRESSION; REAL-WORLD; RELEVANCE RANKING; SCENE OBJECT; SPATIAL MANAGEMENT;

AUGMENTED REALITY; INFORMATION RETRIEVAL; LEARNING SYSTEMS; LOGISTICS; REGRESSION ANALYSIS; USER INTERFACES; VIRTUAL REALITY;

TARGET TRACKING;

EID: 77952388138     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1743666.1743692     Document Type: Conference Paper

References (12)

1. HARDOON, D., SHAWE-TAYLOR, J., AJANKI, A., PUOLAMÄKI, K., AND KASKI, S. 2007. Information retrieval by inferring implicit queries from eye movements. In International Conference on Artificial Intelligence and Statistics (AISTATS '07).
2. HENDERSON, J. M. 2003. Human gaze control during real-world scene perception. Trends in Cognitive Sciences 7, 11, 498-504.
3. HYRSKYKARI, A., MAJARANTA, P., AALTONEN, A., AND RÄIHÄ, K.-J. 2000. Design issues of 'idict': A gaze-assisted translation aid. In Proceedings of ETRA 2000, Eye Tracking Research and Applications Symposium, ACM Press, ACM Press, 9-14.
4. ITTI, L., KOCH, C., AND NIEBUR, E. 1998. A model of saliencybased visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 11, 1254-1259.
5. KANDEMIR, M., SAARINEN, V.-M., AND KASKI, S. 2010. Inferring object relevance from gaze in dynamic scenes. In To Appear in Short Paper Proceedings of ETRA 2000, Eye Tracking Research and Applications Symposium.
6. KLAMI, A., SAUNDERS, C., DE CAMPOS, T. E., AND KASKI, S. 2008. Can relevance of images be inferred from eye movements? In MIR '08: Proceeding of the 1st ACM international conference on Multimedia information retrieval, ACM, New York, NY, USA, 134-140.
7. KOZMA, L., KLAMI, A., AND KASKI, S. 2009. GaZIR: Gazebased zooming interface for image retrieval. In Proc. ICMIMLMI 2009, The Eleventh International Conference on Multimodal Interfaces and The Sixth Workshop on Machine Learning for Multimodal Interaction, ACM, New York, NY, USA, 305-312.
8. MCCULLAGH, P., AND NELDER, J. 1989. Generalized Linear Models. Chapman & Hall/CRC.
9. QVARFORDT, P., AND ZHAI, S. 2005. Conversing with the user based on eye-gaze patterns. In CHI '05: Proceedings of the SIGCHI conference on Human factors in computing systems, ACM, New York, NY, USA, 221-230.
10. TORRALBA, A., OLIVA, A., CASTELHANO, M. S., AND HENDERSON, J. M. 2006. Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search. Psychological Review 113, 4, 766-786.
11. WARD, D. J., AND MACKAY, D. J. C. 2002. Fast hands-free writing by gaze direction. Nature 418, 6900, 838.
12. ZHANG, L., TONG, M. H., MARKS, T. K., SHAN, H., AND COTTRELL, G. W. 2008. Sun: A bayesian framework for saliency using natural statistics. Journal of Vision 8, 7 (12), 1-20.