Accurately and automatically detecting image orientation is a task of great importance in intelligent image processing. In this paper, we present automatic image orientation detection algorithms based on these features: color moments; harris corner; phase symmetry; edge direction histogram. The statistical learning support vector machines, AdaBoost, Subspace classifier are used in our approach as classifiers. 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 approaches. Discussions and future directions for this work are also addressed at the end of the paper.

Detector Of Image Orientation Based On Borda-count

NANNI, LORIS;LUMINI, ALESSANDRA
2005

Abstract

Accurately and automatically detecting image orientation is a task of great importance in intelligent image processing. In this paper, we present automatic image orientation detection algorithms based on these features: color moments; harris corner; phase symmetry; edge direction histogram. The statistical learning support vector machines, AdaBoost, Subspace classifier are used in our approach as classifiers. 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 approaches. Discussions and future directions for this work are also addressed at the end of the paper.
Pattern Recognition and Image Analysis Second Iberian Conference, IbPRIA 2005, Estoril, Portugal, June 7-9, 2005, Proceeding, Part II
231
238
L. Nanni; A. Lumini
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/6797
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