Image Salient Object Detection Based on K-means and Level Set Superpixel Segmentation
Jindiao Huang, Xing Jin, Jingjing Zhang · Journal of Physics Conference Series · 2020
Abstract In order to detect and segment the salient objects in digital images to solve complex machine visual problems, this paper proposes an image salient object detection algorithm based on K-means and level set superpixel segmentation. The algorithm segments a given target image into multiple superpixel regions with similar features to abstract unnecessary details in the images, and reduce the number of colors in all superpixels by Histogram acceleration to increase computational efficiency. The saliency map obtained by calculating the distance of all superpixel in the Lab color space, and optimizes the detection effect by background prior and multi-scale spatial fusion. A large number of experiments show that the saliency detection method proposed in this paper is superior to algorithms such as SR, AC, FT, MSS, LC, CA, SF, HC and RC in accuracy, recall rate, AUC value and average absolute error.