One of the most straightforward, direct and efficient approaches to Image Segmentation isImage Thresholding. Multi-level Image Thresholding is an essential viewpoint in many image processing andPattern Recognition based real-time applications which can effectively and efficiently classify the pixels intovarious groups denoting multiple regions in an Image. Thresholding based Image Segmentation using fuzzyentropy combined with intelligent optimization approaches are commonly used direct methods to properlyidentify the thresholds so that they can be used to segment an Image accurately. In this paper a novel approachfor multi-level image thresholding is proposed using Type II Fuzzy sets combined with Adaptive PlantPropagation Algorithm (APPA). Obtaining the optimal thresholds for an image by maximizing the entropy isextremely tedious and time consuming with increase in the number of thresholds. Hence, Adaptive PlantPropagation Algorithm (APPA), a memetic algorithm based on plant intelligence, is used for fast and efficientselection of optimal thresholds. This fact is reasonably justified by comparing the accuracy of the outcomes andcomputational time consumed by other modern state-of-the-art algorithms such as Particle SwarmOptimization (PSO), Gravitational Search Algorithm (GSA) and Genetic Algorithm (GA).
A Type–II Fuzzy Entropy Based Multi-Level Image Thresholding Using Adaptive Plant Propagation Algorithm
