Plants Leaf Segmentation using Bacterial Foraging Optimization algorithm

Computer vision methodologies has been predominantly used for solving number of problems related with pattern recognition, machine vision, object detection, object classification and many more. This terminology along with soft computing approaches have proven to be a solution for various complex problems. But, in recent time it has been noticed that when conventional methods incorporated with optimization methods, the result achieves higher accuracy and also lessens the computational time. These Nature inspired algorithm (NIA) or Bio-inspired methods have been espoused in the number of applications for finding an accurate solution working with real-time or complex data. Image segmentation is one of them. It is the practice of separating an image into its constituent subparts in order to extract some meaningful information from it. There are number of methods used for performing segmentation. In this paper our aim is in studying one of the optimization method named as Bacterial Foraging Optimization Algorithm (BFOA). This method is then used for initializing the weights for Artificial Neural Network (ANN) that is deployed for image segmentation. Finally, when the results are compared with the other methods shows the efficiency BFO-ANN method.