Accurately and automatically detecting image orientation is a task of great importance in intelligent image processing. In this paper, we present an automatic image orientation detection algorithm based on low-level features: color moments; harris corner; phase symmetry; edge direction histogram. Support vector machines, statistical classifiers, parzen window classifiers are used in our approach: we use Borda Count as combination rule for these classifiers. Large amounts of experiments have been conducted, on a database of more than 6,000 images of real photos, to validate our approach. Discussions and future directions for this work are also addressed at the end of the paper.

Detector of Image Orientation based on Borda-count

LUMINI, ALESSANDRA;NANNI, LORIS
2006

Abstract

Accurately and automatically detecting image orientation is a task of great importance in intelligent image processing. In this paper, we present an automatic image orientation detection algorithm based on low-level features: color moments; harris corner; phase symmetry; edge direction histogram. Support vector machines, statistical classifiers, parzen window classifiers are used in our approach: we use Borda Count as combination rule for these classifiers. Large amounts of experiments have been conducted, on a database of more than 6,000 images of real photos, to validate our approach. Discussions and future directions for this work are also addressed at the end of the paper.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/30113
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