Please use this identifier to cite or link to this item: https://doi.org/10.1145/1873951.1874047
Title: Making computers look the way we look: Exploiting visual attention for image understanding
Authors: Katti, H. 
Subramanian, R.
Kankanhalli, M. 
Sebe, N.
Chua, T.-S. 
Ramakrishnan, K.R.
Keywords: abstract
eye-tracker
fixations
salient regions
visual attention
Issue Date: 2010
Source: Katti, H.,Subramanian, R.,Kankanhalli, M.,Sebe, N.,Chua, T.-S.,Ramakrishnan, K.R. (2010). Making computers look the way we look: Exploiting visual attention for image understanding. MM'10 - Proceedings of the ACM Multimedia 2010 International Conference : 667-670. ScholarBank@NUS Repository. https://doi.org/10.1145/1873951.1874047
Abstract: Human Visual attention (HVA) is an important strategy to focus on specific information while observing and understanding visual stimuli. HVA involves making a series of fixations on select locations while performing tasks such as object recognition, scene understanding, etc. We present one of the first works that combines fixation information with automated concept detectors to (i) infer abstract image semantics, and (ii) enhance performance of object detectors. We develop visual attention-based models that sample fixation distributions and fixation transition distributions in regions-of-interest (ROI) to infer abstract semantics such as expressive faces and interactions (such as look, read, etc.). We also exploit eye-gaze information to deduce possible locations and scale of salient concepts and aid state-of-art detectors. A 18% performance increase with over 80% reduction in computational time for a state-of-art object detector [4]. © 2010 ACM.
Source Title: MM'10 - Proceedings of the ACM Multimedia 2010 International Conference
URI: http://scholarbank.nus.edu.sg/handle/10635/41381
ISBN: 9781605589336
DOI: 10.1145/1873951.1874047
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