This work is based on a fairly old research paper. Since then, there has been significant progress on detecting dogs and cats more reliably.
Two prominent groups that are working on it are Andrew Zisserman's group at Oxford: www.robots.ox.ac.uk/~vgg/publications/2012/parkhi12a/parkhi12a.pdf
And Peter Belhumeur's group at Columbia University, who'll be presenting a new paper on detecting and recognizing dog breeds at the European Conference on Computer Vision (ECCV) in 2 weeks. [Peter was my PhD advisor]
More generally, many people in computer vision are getting excited about "fine-grained visual categorization," which is about classifying things at roughly the "species" level. This is in contrast to a lot of the previous computer vision literature, which either focused on generic categories (e.g., people vs animal vs car vs rocket-propelled-grenade) or specific object/instance recognition (e.g., face recognition).
The drag-drop isn't working for me (Chrome Version 22.0.1229.79 on Unbuntu). It would be nice to have fallback to something simpler and more robust - i.e. a way to paste in a URL.
It looks like it loves ears on an upright cat. If the contrast between the angle of the ear and the background is clear it will eagerly draw boxes around them. I have lots of photos of my cat :
Had the samme issue. Chrome 21 (and now 22 after checking version). Happened when I dragged and dropped an image from another Chrome window. Worked fine when dropping a local image.
Two prominent groups that are working on it are Andrew Zisserman's group at Oxford: www.robots.ox.ac.uk/~vgg/publications/2012/parkhi12a/parkhi12a.pdf
And Peter Belhumeur's group at Columbia University, who'll be presenting a new paper on detecting and recognizing dog breeds at the European Conference on Computer Vision (ECCV) in 2 weeks. [Peter was my PhD advisor]
More generally, many people in computer vision are getting excited about "fine-grained visual categorization," which is about classifying things at roughly the "species" level. This is in contrast to a lot of the previous computer vision literature, which either focused on generic categories (e.g., people vs animal vs car vs rocket-propelled-grenade) or specific object/instance recognition (e.g., face recognition).