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Cited by 369

The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.

An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed

Navalpakkam, V., Itti, L.·2006·2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06)
DOI 10.1109/cvpr.2006.54
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Abstract

Integration of goal-driven, top-down attention and image-driven, bottom-up attention is crucial for visual search. Yet, previous research has mostly focused on models that are purely top-down or bottom-up. Here, we propose a new model that combines both. The bottom-up component computes the visual salience of scene locations in different feature maps extracted at multiple spatial scales. The topdown component uses accumulated statistical knowledge of the visual features of the desired search target and background clutter, to optimally tune the bottom-up maps such that target detection speed is maximized. Testing on 750 artificial and natural scenes shows that the model’s predictions are consistent with a large body of available literature on human psychophysics of visual search. These results suggest that our model may provide good approximation of how humans combine bottom-up and top-down cues such as to optimize target detection speed.

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Metadata

Authors
Navalpakkam, V., & Itti, L.
Venue
2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06)
Publisher
IEEE
Published
2006-07-10
Volume
2
Pages
2049-2056
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2006.54
OpenAlex
W2145227859
Cited by
369
References
32
FWCI
14.14
Type
conference paper
Language
en
Open access
closed

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