REU Presentation Week 3Nicholas Baker

What features “pop out” in a scene?

No prior information/goal

Identify areas of large feature contrasts in center-surround condition Luminance, color, orientation, motion

Bottom Up Visual Salience

Identify areas of high intrinsic dimensionality by analyzing the signal as Shannon information (Vig 2012)

Identify areas of low level surprisal in a scene (Itti 2005)

Weight continuity and visual clutter as well as local feature contrasts (He 2011)

Separate feature matrix into low rank non-salient matrix and sparse salient matrix (Souly)

Bottom up Visual Salience in Computer Vision

Goal driven analysis of scene

Direct visual attention to area/features of probable importance

Locate objects/actions/features of exogenous significance

Top Down Visual Salience

Use CRF modulated dictionary learning to construct top down saliency map (Yang 2012)

Use online Reinforced Learning to interactively teach machine how to correctly allocate attention using U-Tree algorithm (Borji 2009)

Top Down Visual Salience in Computer Vision

Most current top-down visual saliency work is on static images

Choose one promising top-down method for static images

Implement the algorithm if code is not available

Extend it to perform on videos instead of static images

My Work