Multilevel Image Segmentation Based on Fractional-Order Darwinian Particle Swarm Optimization

Abstract

Hyperspectral remote sensing images contain hundreds of data channels. Due to the high dimensionality of the hyperspectral data, it is difficult to design accurate and efficient image segmentation algorithms for such imagery. In this paper, a new multilevel thresholding method is introduced for the segmentation of hyperspectral and multispectral images. The new method is based on Fractional-Order Darwinian Particle Swarm Optimization (FODPSO) which exploits the many swarms of test solutions that may exist at any time. In addition, the concept of fractional derivative is used to control the convergence rate of particles. In this work, the socalled Otsu problem is solved for each channel of the multispectral and hyperspectral data. Therefore, the problem of -level thresholding is reduced to an optimization problem in order to search for the thresholds that maximizes the between-class variance. Experimental results are favorable for the FODPSO when compared to other bio-inspired methods for multi-level segmentation of multispectral and hyperspectral images. The FODPSO presents a statistically significant improvement in terms of both CPU time and fitness value, i.e., the approach is able to find the optimal set of thresholds with a larger between-class variance in less computational time than the other approaches. In addition, a new classification approach based on Support Vector Machine (SVM) and FODPSO is introduced in this paper. Results confirm that the new segmentation method is able to improve upon results obtained with the standard SVM in terms of classification accuracies.

Multi-Patches Iris Based Person Authentication System Using Particle Swarm Optimization and Fuzzy C-Means Clustering

“…A simple modified PSO is proposed by Lee et al (Lee et al, 2012) to extract both low-level features and high-level image semantics from the color image. Tillet et al (Tillett et al, 2005) have introduced Darwinien PSO, Ghamisi et al (Ghamisi et al, 2014) have devised fractional-order Darwinien PSO and these techniques are evaluated on medical images (Ryalat et al, 2016). In the biometrics domain, Parez et al have used PSO to generate the templates for face and iris localization (Perez et al, 2010) and Chen and Chu have combined probabilistic neural network and PSO to design an optimized classifier model for iris recognition (Chen and Chu, 2009).…”

“…It consists of a group of pixels in an image that collectively move in the neighbourhood in search of the global optimum (Ghamisi et al, 2014). Like any other genetic algorithm (GA), PSO is initialized with a population of random solutions.…”

Swarm Optimization Techniques for Segmenting Gel Electrophoresis Images

“…This technique needs to be developed to enhance the system performance. Ghamisi et al (2014) established segmentation method depending on FODPSO along with Support Vector Machine (SVM) for remote sensing image to solve many optimization problems and Otsu problem. The advantages of this technique are reducing the n-level threshold to detect the optimal thresholds that maximize the variance between classes.…”

Reply to: Comments on “Particle Swarm Optimization with Fractional-Order Velocity”

“…The correct formula was initially proposed in [6,7]. The formula adopted in our algorithms discussed in our papers [1,3,4,8] is, in fact, the following:The results in [5] and [8] are not directly comparable. In [5], the charts are obtained by running a single test; while in [8], the charts are obtained by the median of 201 simulations tests.…”

“…The correct formula was initially proposed in [6,7]. The formula adopted in our algorithms discussed in our papers [1,3,4,8] is, in fact, the following:…”