<title>Wavelet-based hierarchical organization of large image databases: ISAR and face recognition</title>

Abstract

ISR develops, applies and teaches advanced methodologies of design and analysis to ABSTRACTWe present a method for constructing efficient hierarchical organization of image databases for fast recognition and classification. The method combines a wavelet preprocessor with a Tree-Structured-Vector-Quantization for clustering. We show results of application of the method to ISAR data from ships and to face recognition based on photograph databases. In the ISAR case we show how the method constructs a multi-resolution aspect graph for each target.

Adaptive classification based on compressed data using learning vector quantization

“…To date, we have utilized wavelets as the multiresolution preprocessor and Tree-structured-vectorquantization (TSVQ) as the clustering postprocessor. We have applied the resulting WTSVQ algorithm to various ATR problems based on radar [4] [5] [SI, ISAR and face recognition problems [7]. We have established similar results on ATR based on FLIR using polygonization of object silhouettes [SI [9] as the multiresolution preprocessor.…”

“…In this extension, we use and extend the methods and analysis of [19]. With this extension, we will be able to treat the performance of the WTSVQ algorithm of [4] [5] [6], [7] analytically including compression of the wavelet coefficients.…”