Open Access
Learning Saliency Maps for Object Categorization
Franck Moosmann,Diane Larlus,Frédéric Jurie +2 more
- 13 May 2006
pp 1-15
TL;DR: A novel approach for object category recognition that can find objects in challenging conditions using visual attention technique that combines saliency maps very closely with the extraction of random subwindows for classification purposes.
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Abstract: We present a novel approach for object category recognition that can find objects in challenging conditions using visual attention technique. It combines saliency maps very closely with the extraction of random subwindows for classification purposes. The maps are built online by the classifier while being used by it to classify the image.
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Citations
Salient Object Detection: A Benchmark
TL;DR: In this article, 40 state-of-the-art models (28 salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) were evaluated over 6 challenging datasets for the purpose of benchmarking salient object detector and segmentation methods.
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Salient Object Detection: A Discriminative Regional Feature Integration Approach
Huaizu Jiang,Jingdong Wang,Zejian Yuan,Yang Wu,Nanning Zheng,Shipeng Li +5 more
- 23 Jun 2013
TL;DR: This paper regards saliency map computation as a regression problem, which is based on multi-level image segmentation, and uses the supervised learning approach to map the regional feature vector to a saliency score, and finally fuses the saliency scores across multiple levels, yielding the salency map.
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Measuring the Objectness of Image Windows
TL;DR: In this paper, a generic objectness measure is proposed to quantify how likely an image window is to contain an object of any class, such as cows and telephones, from amorphous background elements such as grass and road.
Salient Object Detection: A Benchmark
TL;DR: It is found that the models designed specifically for salient object detection generally work better than models in closely related areas, which provides a precise definition and suggests an appropriate treatment of this problem that distinguishes it from other problems.
Measuring the objectness of image windows
Bogdan Alexe,Thomas Deselaers,Vittorio Ferrari +2 more
- 01 Aug 2011
TL;DR: A generic objectness measure, quantifying how likely it is for an image window to contain an object of any class, and uses objectness as a complementary score in addition to the class-specific model, which leads to fewer false positives.
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References
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Chris Harris,Mike Stephens +1 more
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A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
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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 tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesian tracking
Simon Maskell,Neil Gordon +1 more
- 01 Jan 2001
TL;DR: Both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters are reviewed.