Saliency detection via divergence analysis: a unified perspective

Computational modeling of visual attention has been a very active area over the past few decades. Numerous models and algorithms have been proposed to detect salient regions in images and videos. We present a unified view of various bottom-up saliency detection algorithms. As these methods were proposed from intuition and principles inspired from psychophysical studies of human vision, the theoretical relations among them are unclear. In this thesis, we provide such a bridge. The saliency is defined in terms of divergence between feature distributions estimated using samples from center and surround, respectively. We explicitly show that these seemingly different algorithms are in fact closely related and derive conditions under which the methods are equivalent. We also discuss some commonly-used center-surround selection strategies. Comparative experiments on two benchmark datasets are presented to provide further insights on relative advantages of these algorithms.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2014

Author dc:creator

• Huang, Jia-Bin

Contributors dc:contributor

• Ahuja, Narendra

• Saliency Detection
• Object detection
• Visual Attention
• Divergence

Statement dc:rights

• Copyright 2013 Jia-Bin Huang

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/46599

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/46599