A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Proceedings of the First International Workshop on Multiple Classifier Systems
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Computer Recognition of Chest Image Orientation
CBMS '98 Proceedings of the Eleventh IEEE Symposium on Computer-Based Medical Systems
Automatic Absolute Orientation of Scanned Aerial Photographs
SIBGRAPHI '98 Proceedings of the International Symposium on Computer Graphics, Image Processing, and Vision
Boosting Image Orientation Detection with Indoor vs. Outdoor Classification
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Detecting image orientation based on low-level visual content
Computer Vision and Image Understanding
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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.