Learning to predict where humans look
Tilke Judd,Krista A. Ehinger,Frédo Durand,Antonio Torralba +3 more
- pp 2106-2113
TLDR
This paper collects eye tracking data of 15 viewers on 1003 images and uses this database as training and testing examples to learn a model of saliency based on low, middle and high-level image features.Abstract:
For many applications in graphics, design, and human computer interaction, it is essential to understand where humans look in a scene Where eye tracking devices are not a viable option, models of saliency can be used to predict fixation locations Most saliency approaches are based on bottom-up computation that does not consider top-down image semantics and often does not match actual eye movements To address this problem, we collected eye tracking data of 15 viewers on 1003 images and use this database as training and testing examples to learn a model of saliency based on low, middle and high-level image features This large database of eye tracking data is publicly available with this paperread more
Citations
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Proceedings ArticleDOI
Global contrast based salient region detection
TL;DR: This work proposes a regional contrast based saliency extraction algorithm, which simultaneously evaluates global contrast differences and spatial coherence, and consistently outperformed existing saliency detection methods.
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Learning to Detect a Salient Object
TL;DR: A set of novel features, including multiscale contrast, center-surround histogram, and color spatial distribution, are proposed to describe a salient object locally, regionally, and globally.
Proceedings ArticleDOI
Saliency Detection via Graph-Based Manifold Ranking
TL;DR: This work considers both foreground and background cues in a different way and ranks the similarity of the image elements with foreground cues or background cues via graph-based manifold ranking, defined based on their relevances to the given seeds or queries.
Journal ArticleDOI
State-of-the-Art in Visual Attention Modeling
Ali Borji,Laurent Itti +1 more
TL;DR: A taxonomy of nearly 65 models of attention provides a critical comparison of approaches, their capabilities, and shortcomings, and addresses several challenging issues with models, including biological plausibility of the computations, correlation with eye movement datasets, bottom-up and top-down dissociation, and constructing meaningful performance measures.
Journal ArticleDOI
Context-Aware Saliency Detection
TL;DR: A new type of saliency is proposed—context-aware saliency—which aims at detecting the image regions that represent the scene, and a detection algorithm is presented which is based on four principles observed in the psychological literature.
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