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Jiri Matas

Center for Machine Perception
School of Electrical Engineering
Department of Cybernetics
Czech Technical University, Prague


Sub-linear Indexing for Large Scale Object Recognition

Realistic approaches to large scale object recognition, i.e. for detection and localisation of hundreds or more objects, must support sub-linear time indexing. A method capable of recognising one of N objects in log(N) time will be presented. The "visual memory" is organised as a binary decision tree that is built to minimise average time to decision. Leaves of the tree represent a few local patches, and each non-terminal node is associated with a weak classifier. In the recognition phase, a single invariant measurement on a query patch decides in which subtree a corresponding patch is sought. The method possesses all the strengths of local affine region methods robustness to background clutter, occlusion, and large changes of viewpoints. Experimentally we show that it supports near real-time recognition of hundreds of objects with state-of-the-art recognition rates. After the test image is processed (in a second on a current PCs), the recognition via indexing into the visual memory requires milliseconds.


Friday, June 16, 2006
JEC 3117 - 4:00 p.m. to 5:00 p.m.
Refreshments at 3:30 p.m.


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