Context-aware saliency detection
Stas Goferman,Lihi Zelnik-Manor,Ayellet Tal +2 more
- pp 2376-2383
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TLDR
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.Abstract:
We propose a new type of saliency – context-aware saliency – which aims at detecting the image regions that represent the scene. This definition differs from previous definitions whose goal is to either identify fixation points or detect the dominant object. In accordance with our saliency definition, we present a detection algorithm which is based on four principles observed in the psychological literature. The benefits of the proposed approach are evaluated in two applications where the context of the dominant objects is just as essential as the objects themselves. In image retargeting we demonstrate that using our saliency prevents distortions in the important regions. In summarization we show that our saliency helps to produce compact, appealing, and informative summaries.read more
Citations
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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.
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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.
Proceedings ArticleDOI
Saliency filters: Contrast based filtering for salient region detection
TL;DR: A conceptually clear and intuitive algorithm for contrast-based saliency estimation that outperforms all state-of-the-art approaches and can be formulated in a unified way using high-dimensional Gaussian filters.
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.
Proceedings ArticleDOI
Hierarchical Saliency Detection
TL;DR: This work tackles saliency detection from a scale point of view and proposes a multi-layer approach to analyze saliency cues, by finding saliency values optimally in a tree model.
References
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Proceedings ArticleDOI
Rapid object detection using a boosted cascade of simple features
Paul A. Viola,Michael Jones +1 more
TL;DR: A machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates and the introduction of a new image representation called the "integral image" which allows the features used by the detector to be computed very quickly.
Journal ArticleDOI
A feature-integration theory of attention
Anne Treisman,Garry A. Gelade +1 more
TL;DR: A new hypothesis about the role of focused attention is proposed, which offers a new set of criteria for distinguishing separable from integral features and a new rationale for predicting which tasks will show attention limits and which will not.
Journal ArticleDOI
A model of saliency-based visual attention for rapid scene analysis
TL;DR: In this article, a visual attention system inspired by the behavior and the neuronal architecture of the early primate visual system is presented, where multiscale image features are combined into a single topographical saliency map.
A model of saliency-based visual attention for rapid scene analysis
TL;DR: A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented, which breaks down the complex problem of scene understanding by rapidly selecting conspicuous locations to be analyzed in detail.
Journal ArticleDOI
"GrabCut": interactive foreground extraction using iterated graph cuts
TL;DR: A more powerful, iterative version of the optimisation of the graph-cut approach is developed and the power of the iterative algorithm is used to simplify substantially the user interaction needed for a given quality of result.