An Empirical Comparison of SNoW and SVMs for Face Detection

Impressive claims have been made for the performance of the SNoW algorithm on face detection tasks by Yang et. al. [7]. In particular, by looking at both their results and those of Heisele et. al. [3], one could infer that the SNoW system performed substantially better than an SVM-based system, even when the SVM used a polynomial kernel and the SNoW system used a particularly simplistic 'primitive' linear representation. We evaluated the two approaches in a controlled experiment, looking directly at performance on a simple, fixed-sized test set, isolating out 'infrastructure' issues related to detecting faces at various scales in large images. We found that SNoW performed about as well as linear SVMs, and substantially worse than polynomial SVMs.

Japanese Scientific And Technology Information in the United States - Public Policy and Private Initiatives

In January 1983 a group of US government, industry and university information specialists gathered at MIT to take stock of efforts to monitor, acquire, assess, and disseminate Japanese scientific and technical information (JSTI). It was agreed that these efforts were uncoordinated and poorly conceived, and that a clearer understanding of Japanese technical information systems and a clearer sense of its importance to end users was necessary. That meeting led to formal technology assessments, Congressinal hearings, and legislation; it also helped stimulate several private initiatives in JSTI provision. Four years later there exist better coordinated and better conceived JSTI programs in both the public and private sectors, but there remains much room for improvement. This paper will recount their development and assess future directions.