  One of the most straightforward, direct and efficient approaches to Image
Segmentation is Image Thresholding. Multi-level Image Thresholding is an
essential viewpoint in many image processing and Pattern Recognition based
real-time applications which can effectively and efficiently classify the
pixels into various groups denoting multiple regions in an Image. Thresholding
based Image Segmentation using fuzzy entropy combined with intelligent
optimization approaches are commonly used direct methods to properly identify
the thresholds so that they can be used to segment an Image accurately. In this
paper a novel approach for multi-level image thresholding is proposed using
Type II Fuzzy sets combined with Adaptive Plant Propagation Algorithm (APPA).
Obtaining the optimal thresholds for an image by maximizing the entropy is
extremely tedious and time consuming with increase in the number of thresholds.
Hence, Adaptive Plant Propagation Algorithm (APPA), a memetic algorithm based
on plant intelligence, is used for fast and efficient selection of optimal
thresholds. This fact is reasonably justified by comparing the accuracy of the
outcomes and computational time consumed by other modern state-of-the-art
algorithms such as Particle Swarm Optimization (PSO), Gravitational Search
Algorithm (GSA) and Genetic Algorithm (GA).
