In this paper, we consider the problem of segmenting an image into sky and non-sky regions, typically referred to as horizon line detection or skyline extraction. Specifically, we present a new approach to horizon line detection by coupling machine learning with dynamic programming. Given an image, the Canny edge detector is applied first and keeping only those edges which survive over a wide range of thresholds. We refer to the surviving edges as Maximally Stable Extremal Edges (MSEEs). The number of edges is further reduced by classifying MSEEs into horizon and non-horizon edges using a Support Vector Machine (SVM) classifier. Dynamic programming is then applied on the horizon classified edges to extract the horizon line. Various local texture features and their combinations have been investigated in training the horizon edge classifier including SIFT, LBP, HOG, SIFT-LBP, SIFT-HOG, LBP-HOG and SIFT-LBP-HOG. We have also investigated various nodal costs in the context of dynamic programming including binary edge scores, normalized edge classification scores, gradient magnitude and their combinations. The proposed approach has been evaluated and compared with a competitive approach on two challenging data sets, illustrating superior performance.
A Machine Learning Approach to Horizon Line Detection Using Local Features
