Experiments on (Intelligent) Brute Force Methods for Appearance-Based Object Recognition

It has long been recognized that, in principle object recognition problems can be solved by simple, brute force methods. However, the approach has generally been held to be completely impractical. We argue that by combining a few more or less standard tricks with computational resources that are historically large, but completely feasible by recent standards , dramatic results can be achieved for a number of recognition problems. In particular, we describe a resource-intensive, appearance-based method that utilizes intermediate-level features to provide normalized keys into a large, memorized feature database, and Bayesian evidence combination coupled with a Hough-like indexing scheme to assemble object hypotheses from the memory. This system demonstrates robust recognition of a variety of 3-D shapes, ranging from sports cars and ghter planes to snakes and lizards over full spherical or hemispherical ranges (and pla-nar scale, translation and rotation). We report the results of various large-scale performance tests, involving , altogether, over 2000 separate test images. These include performance scaling with database size, robustness against clutter, and generic ability. The result of 97% forced choice accuracy with full ortho-graphic invariance for 24 complex curved 3-D objects over full viewing spheres or hemispheres is the best we are aware of for this type of problem.