Year: 2005
Type: conference-paper
Abstract: This paper proposes a method for computing fast approximations to sup-port vector decision functions in the field of object detection. In the present approach we are building on an existing algorithm where the set of support vectors is replaced by a smaller, so-called reduced set of syn-thesized input space points. In contrast to the existing method that finds the reduced set via unconstrained optimization, we impose a structural constraint on the synthetic points such that the resulting approximations can be evaluated via separable filters. For applications that require scan-ning large images, this decreases the computational complexity by a sig-nificant amount. Experimental results show that in face detection, rank deficient approximations are 4 to 6 times faster than unconstrained re-duced set systems. 1
Source: MPG.PuRe (Max Planck Society)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2122044381