REU Presentation Week 3. Nicholas Baker. Bottom Up Visual Salience. 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 in Computer Vision.
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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)
Goal driven analysis of scene
Direct visual attention to area/features of probable importance
Locate objects/actions/features of exogenous significance
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)
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