Bayberry image segmentation based on manifold ranking salient object detection method

A bayberry image segmentation method based on salient object detection is proposed in this study. First, an image enhancement algorithm, homomorphic filtering, was used for light compensation. Second, superpixels were used as the basic unit in the subsequent significant calculation with the SLIC (simple linear iterative clustering) algorithm and were constructed as nodes in an Undirected Graphical Model G (V,E). Third, a two-stage scheme of saliency detection was proposed. In the first stage, the nodes on each boundary were separately ranked as query nodes. Then, four saliency maps were obtained using a MR (manifold ranking) method. After combining the four saliency maps, the saliency map of the first stage was acquired. In the second stage, binary segmentation was performed, based on the saliency map of the first stage to obtain the target query. Finally, binary segmentation was performed based on the saliency map with the Otsu's method to achieve the segmentation of bayberry images. The average values of Precision, Recall and F-measure in the proposed algorithm are 0.93, 0.83 and 0.90, respectively. Experimental results show that the proposed segmentation algorithm performs more effectively than Otsu's and K-means segmentation algorithms. (C) 2018 IAgrE. Published by Elsevier Ltd. All rights reserved.