A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the First International Workshop on Multiple Classifier Systems
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
Detecting image orientation based on low-level visual content
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Upright orientation of man-made objects
ACM SIGGRAPH 2008 papers
Soft clustering for nonparametric probability density function estimation
Pattern Recognition Letters
Beyond Travel & Tourism competitiveness ranking using DEA, GST, ANN and Borda count
Expert Systems with Applications: An International Journal
Unsupervised upright orientation of man-made models
Graphical Models
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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 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 6000 images of real photos, to validate our approach. Discussions and future directions for this work are also addressed at the end of the paper.