I came across a paper the other day by Serge Belongie's group at UCSD which has a simple, yet clever, solution of how to incorporate "common sense" information about what objects belong in a scene to improve computer vision algorithms. Basically, if you have an idea of some of the objects detected in a scene, you can use Google Sets to increase the probability of detecting related objects. For example, it's easier to determining if an object is a lemon or a tennis ball if you're also detecting a tennis racket and person in the same image.
http://www.cs.ucsd.edu/~sjb/iccv2007a.pdf http://www.sciencedaily.com/releases/2007/10/071017174328.htm http://labs.google.com/sets The research is still quite early, but could Google Sets also be useful for more general AI tasks? -- Neil ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=8660244&id_secret=63889787-7348b4
