VASC Seminar : Object Recognition by Scene Alignment

Bryan Russell
MIT
Monday, Nov 26, 3:30pm, NSH 1507

Current object recognition systems can only recognize a limited number of object categories; scaling up to many categories is the next challenge inobject recognition. We seek to build a system to recognize and localize many different object categories in complex scenes. We achieve thisthrough a deceptively simple approach: by matching the input image, in anappropriate representation, to images in a large training set of labeled images. This gives us a set of retrieval images, which provide hypothesesfor object identities and locations. We combine this knowledge from theretrieval images with an object detector to detect objects in the image. The simplicity of the approach allows learning for a large number ofobject classes embedded in many different scenes. We demonstrate improvedclassification and localization performance over a standard objectdetector using a held-out test set from the Label Me database.Furthermore, our system restricts the object search space and therefore greatly increases computational efficiency.

Bio:
After leaving sunny Phoenix, AZ, Bryan received his A.B. from DartmouthCollege. He recently defended his dissertation "Labeling, Discovering,and Detecting Objects in Images" at MIT under the supervision of WilliamFreeman and Antonio Torralba. His next journey will be as a post-doctoral fellow at Ecole Normale Supérieure under Jean Ponce and Andrew Zisserman.There, he will continue to pursue research in visual object recognitionand scene understanding.