A Type II Fuzzy Entropy Based Multi-Level Image Thresholding Using Adaptive Plant Propagation Algorithm

摘要

One of the most straightforward, direct and efficient approaches to ImageSegmentation is Image Thresholding. Multi-level Image Thresholding is anessential viewpoint in many image processing and Pattern Recognition basedreal-time applications which can effectively and efficiently classify thepixels into various groups denoting multiple regions in an Image. Thresholdingbased Image Segmentation using fuzzy entropy combined with intelligentoptimization approaches are commonly used direct methods to properly identifythe thresholds so that they can be used to segment an Image accurately. In thispaper a novel approach for multi-level image thresholding is proposed usingType II Fuzzy sets combined with Adaptive Plant Propagation 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 Plant Propagation Algorithm (APPA), a memetic algorithm basedon plant intelligence, is used for fast and efficient selection of optimalthresholds. This fact is reasonably justified by comparing the accuracy of theoutcomes and computational time consumed by other modern state-of-the-artalgorithms such as Particle Swarm Optimization (PSO), Gravitational SearchAlgorithm (GSA) and Genetic Algorithm (GA).