Low-resolution photo/drawing classification: metrics, method and archiving optimization

Abstract

Archiving and re-purposing are automated using zoning analysis that performs segmentation (region boundary definition), classification (region typing) and bit-depth determination. For performance throughput reasons, zoning analysis is often performed on a low-resolution (e.g. 50-100 ppi) representation of the document. At these resolutions, heuristic metrics for classification are required. Reported here are metrics for distinguishing photos and color drawings, and a novel classification technique based solely on the statistics of each heuristic metric. The statistical technique allows ready combination of multiple binary classifiers, and provides a lower classification error than simple voting or metric-confidence techniques. This technique permits new metrics to improve the overall classification. The benefit of this technique on archival optimization is shown.