Isolated object Object in context
Isolated object Object in context
Problem 2: search space is HUGE Like finding needles in a haystack Slow(many patches to examine) Error prone(classifier must have very low false positive rate) eed to search over x y locations nd scales s 10,000 patches/object/image 1, 000,000 images/day Plus, we want to do this for 1000 objects
Problem 2: search space is HUGE x 1,000,000 images/day Plus, we want to do this for ~ 1000 objects y s positive rate) “Like finding needles in a haystack” Need to search over x,y locations and scales s - Error prone (classifier must have very low false - Slow (many patches to examine) 10,000 patches/object/image
Solution 2: context can provide a prior on what to look for, and where to look for it Computers/desks unlikely outdoors People most likely here Torralba. IJCV 2003 Talk outline Context-based vision Feature-based object detection Graphical model to combine both sources
Solution 2: context can provide a prior on what to look for, and where to look for it People most likely here Torralba, IJCV 2003 cars 1.0 0.0 n Talk outline • Context-based vision • • pedestria computer desk Computers/desks unlikely outdoors Feature-based object detection Graphical model to combine both sources
Talk outline Context-based vision Feature-based object detection Graphical model to combine both sources Context-based vision · Measure overall scene context or‘gist Use that scene context for Location identification Location categorization Top-down info for object recognition Combine with bottom-up object detection Future focus: training set acquisition
Talk outline • Context-based vision • • Context-based vision • • • Combine with bottom-up object detection • training set acquisition. Feature-based object detection Graphical model to combine both sources Measure overall scene context or “gist” Use that scene context for: – Location identification – Location categorization – Top-down info for object recognition Future focus: