REU Presentation Week 3 Nicholas 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