Information theoretic preattentive saliency: a closed-form solution

TY - GEN

T1 - Information theoretic preattentive saliency

T2 - 1st IEEE Workshop in Information Theory in Computer Vision and Pattern Recognition

AU - Loog, Marco

N1 - Conference code: 1

PY - 2011

Y1 - 2011

N2 - Employing an information theoretic operational definition of bottom-up attention from the field of computational visual perception a very general expression for saliency is provided. As opposed to many of the current approaches to determining a saliency map there is no need for an explicit data-driven density estimation. Given the features descriptors or filter bank that one wants to use to describe the image content at every position we provide a closed-form expression for the associated saliency at that location. This indeed makes explicit that what is considered salient depends on how i.e. by means of which features, image information is described. We illustrate our result by determining a few specific saliency maps based on particular choices of features. One of them makes the link with the mapping underlying well-known Harris interest points, which is a result recently obtained in isolation. Another choice of features is, rather loosely, inspired by the success of histogram of oriented gradient descriptors and proves to provide state-of-the-art results on a collaborative benchmark for region of interest detection.

AB - Employing an information theoretic operational definition of bottom-up attention from the field of computational visual perception a very general expression for saliency is provided. As opposed to many of the current approaches to determining a saliency map there is no need for an explicit data-driven density estimation. Given the features descriptors or filter bank that one wants to use to describe the image content at every position we provide a closed-form expression for the associated saliency at that location. This indeed makes explicit that what is considered salient depends on how i.e. by means of which features, image information is described. We illustrate our result by determining a few specific saliency maps based on particular choices of features. One of them makes the link with the mapping underlying well-known Harris interest points, which is a result recently obtained in isolation. Another choice of features is, rather loosely, inspired by the success of histogram of oriented gradient descriptors and proves to provide state-of-the-art results on a collaborative benchmark for region of interest detection.

UR - http://www.scopus.com/inward/record.url?scp=84856645330&partnerID=8YFLogxK

U2 - 10.1109/ICCVW.2011.6130417

DO - 10.1109/ICCVW.2011.6130417

M3 - Article in proceedings

AN - SCOPUS:84856645330

SN - 978-1-4673-0062-9

SP - 1418

EP - 1424

BT - 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)

PB - IEEE

Y2 - 6 November 2011 through 13 November 2011

ER -