Horizon Detection Using Machine Learning Techniques

Authors:
Sergiy Fefilatyev;Volha Smarodzinava;Lawrence O. Hall;Dmitry B. Goldgof
Affiliations:
University of South Florida, USA;University of South Florida, USA;University of South Florida, USA;University of South Florida, USA
Venue:
ICMLA '06 Proceedings of the 5th International Conference on Machine Learning and Applications
Year:
2006

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Abstract

Detecting a horizon in an image is an important part of many image related applications such as detecting ships on the horizon, flight control, and port security. Most of the existing solutions for the problem only use image processing methods to identify a horizon line in an image. This results in good accuracy for many cases and is fast in computation. However, for some images with difficult environmental conditions like a foggy or cloudy sky these image processing methods are inherently inaccurate in identifying the correct horizon. This paper investigates how to detect the horizon line in a set of images using a machine learning approach. The performance of the SVM, J48, and Naïve Bayes classifiers, used for the problem, has been compared. Accuracy of 90-99% in identifying horizon was achieved on image data set of 20 images.