Elsevier

Vision Research

Volume 50, Issue 14, 25 June 2010, Pages 1338-1352
Vision Research

A Bayesian model for efficient visual search and recognition

https://doi.org/10.1016/j.visres.2010.01.002Get rights and content
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Abstract

Humans employ interacting bottom-up and top-down processes to significantly speed up search and recognition of particular targets. We describe a new model of attention guidance for efficient and scalable first-stage search and recognition with many objects (117,174 images of 1147 objects were tested, and 40 satellite images). Performance for recognition is on par or better than SIFT and HMAX, while being, respectively, 1500 and 279 times faster. The model is also used for top-down guided search, finding a desired object in a 5×5 search array within four attempts, and improving performance for finding houses in satellite images.

Keywords

Recognition
Search
Attention
Feature
Scene analysis

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