A hierarchy of support vector machines for pattern detection
We introduce a computational design for pattern detection based on a tree-structured network of support vector machines (SVMs). An SVM is associated with each cell in a recursive partitioning of the space of patterns (hypotheses) into increasingly finer subsets. The hierarchy is traversed coarse-to-fine and each chain of positive responses from the root to a leaf constitutes a detection. Our objective is to design and build a network which balances overall error and computation. Initially, SVMs are constructed for each cell with no constraints. This "free network" is then perturbed, cell by cell, into another network, which is "graded" in two ways: first, the number of support vectors of each SVM is reduced (by clustering) in order to adjust to a pre-determined, increasing function of cell depth; second, the decision boundaries are shifted to preserve all positive responses from the original set of training data. The limits on the numbers of clusters (virtual support vectors) result from minimizing the mean computational cost of collecting all detections subject to a bound on the expected number of false positives. When applied to detecting faces in cluttered scenes, the patterns correspond to poses and the free network is already faster and more accurate than applying a single pose-specific SVM many times. The graded network promotes very rapid processing of background regions while maintaining the discriminatory power of the free network.
support vector machines statistical learning hierarchy of classifiers coarse-to-fine computation face detection