Polynomial distance classifier correlation filter for pattern recognition

Mohamed Alkanhal; B V K Vijaya Kumar

doi:10.1364/ao.42.004688

Polynomial distance classifier correlation filter for pattern recognition

Appl Opt. 2003 Aug 10;42(23):4688-708. doi: 10.1364/ao.42.004688.

Authors

Mohamed Alkanhal¹, B V K Vijaya Kumar

Affiliation

¹ Electrical and Computer Engineering Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA.

PMID: 13678355
DOI: 10.1364/ao.42.004688

Abstract

We introduce what is to our knowledge a new nonlinear shift-invariant classifier called the polynomial distance classifier correlation filter (PDCCF). The underlying theory extends the original linear distance classifier correlation filter [Appl. Opt. 35, 3127 (1996)] to include nonlinear functions of the input pattern. This new filter provides a framework (for combining different classification filters) that takes advantage of the individual filter strengths. In this new filter design, all filters are optimized jointly. We demonstrate the advantage of the new PDCCF method using simulated and real multi-class synthetic aperture radar images.