抄録
In real world images, many algorithms for adaptive contours detection exist and various improvements to the contours detection have been proposed. The reason for such diversity is that real world images contains heterogeneous mixtures of features and each of the available algorithms exploits some of these features. Thus, depending on the image, different algorithms shows different quality of result. In this paper we propose a method that improves the result adaptive contours detection by using an algorithm selection approach. Previous methods using the algorithm selection approach have been focusing only on images with a particular class of features (artificial, cellular) because of the complexity of real world images. In order to successfully solve this problem we first determine a set of distinctive features of each algorithm using machine learning. Then using these distinctive features we teach an algorithm selector to select best algorithm when a set of features is provided. Finally, we propose a method to split the input image into sub regions that are selected in such a manner that improves the quality of the image processing result. The proposed algorithm is verified on the set of benchmarks and its performance is comparable and better in many cases than the currently best contour detection algorithms.
