A Bayesian model for efficient visual search and recognition.

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 5x5 search array within four attempts, and improving performance for finding houses in satellite images.

journal_name

Vision Res

journal_title

Vision research

authors

Elazary L,Itti L

doi

10.1016/j.visres.2010.01.002

subject

Has Abstract

pub_date

2010-06-25 00:00:00

pages

1338-52

issue

14

eissn

0042-6989

issn

1878-5646

pii

S0042-6989(10)00005-2

journal_volume

50

pub_type

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