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Vanishing point detection based on an artificial bee colony algorithm

Abstract. Vanishing points (VPs) are crucial for inferring the three-dimensional structure of a scene and can be exploited in various computer vision applications. Previous VP detection algorithms have been proven effective but generally cannot guarantee a strong performance in both accuracy and computational time. We propose an artificial bee colony algorithm called dynamic clustering artificial bee colony (DCABC) that accurately and efficiently detects VPs in the image plane. The task is regarded as a dynamic line-clustering problem, and the line clusters are initialized by their orientation information. Inspired by the foraging behavior of bees, DCABC selects the clustering center and reclassifies the line segments based on a distance criterion until the terminating condition is met. The optimal line clusters determine the estimated VP. The dissimilarity among solutions is measured by the Hamming distance between two binary vectors, which simplifies the new solution construction. The performances of the proposed and existing algorithms are evaluated on the York Urban database. The results verify the efficiency and accuracy of our proposed algorithm.