A novel approach for unsupervised image segmentation fusion of plant leaves based on G-mutual information
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ORIGINAL PAPER
A novel approach for unsupervised image segmentation fusion of plant leaves based on G-mutual information Navid Nikbakhsh1 · Yasser Baleghi1
· Hamzeh Agahi2
Received: 14 November 2019 / Revised: 2 June 2020 / Accepted: 15 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Plant leaf segmentation has a very important role in most plant identification methods. Tree leaves segmentation in images with complex background is very difficult when there is no prior information about the leaves and backgrounds. In practice, the parameters of unsupervised image segmentation algorithms must be set for each image to get the best results. In this paper, to overcome this problem, fusion of the results of five leaf segmentation algorithms (fuzzy c-means, SOM and kmeans in various color spaces or different parameters) is applied. To fuse the results of these segmentations, new equations for mutual information (g-mutual information equations) based on the g-calculus are introduced to find the best consensus segmentation. The results of the mentioned primary clustering algorithms are considered as a new feature vector for each pixel. To reduce the time complexity, a fast method is employed using truth table containing different feature vectors. To evaluate this new approach, a leaf image database with natural scenes, taken from Pl@ntLeaves database, is generated to have different positions and orientations. In addition, a widely used database is used to compare the proposed method with other methods. The experimental results presented in this paper show that the use of g-calculus in fusion of image segmentations improves the evaluation parameters. Keywords Pseudo-operations · Image segmentation fusion · G-calculus · Mutual information · Tree leaves segmentation
1 Introduction Plant classification is the foundation of plant ecology, plant medicine, plant genetics and life science. It plays an important role in the protection, development and exploitation of plant resources. Plant classification is not only tedious and time consuming, but also requires special expertise. Image processing and machine vision are electronic engineering techniques that can provide promising tools for automatic plant identification. Machine learning allows the machine to learn from examples and experience. The general goal of machine learning and machine vision is to automate the extraction of information from images, in order to reach high-level understanding from the content of the images
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Yasser Baleghi [email protected]
1
Department of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Shariati Ave, Babol 47148-1167, Iran
2
Department of Mathematics, Faculty of Basic Science, Babol Noshirvani University of Technology, Shariati Ave, Babol 47148-71167, Iran
and perform tasks such as classification and identification of images content. Efficient plant segmentation is one of the most critical steps that can help to accurately label images for detection and control o
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