Background:
Image segmentation is the fundamental step in image processing. Multi-level image segmentation for color image is a very complex and time-consuming process which can be defined as non-deterministic optimization problem. Nature inspired meta-heuristics are best suited to solve such problems. Though several algorithms exist; a modification to suit certain class of engineering problems is always welcome.
Objective:
This paper provides a modified firefly algorithm and its uses for multilevel thresholding in colored images. Opposition based learning is incorporated in the firefly algorithm to improve convergence rate and robustness. Between class variance method of thresholding is used to formulate the objective function.
Method:
Numerous benchmark images are tested for evaluating the performance of proposed method.
Results:
The Experimental results validate the performance of Opposition based improved firefly algorithm (OBIFA) for multi-level image segmentation using peak signal to noise ratio (PSNR) and structured similarity index metric (SSIM)parameter.
Conclusion:
The OBIFA algorithm is best suited for multilevel image thresholding. It provides best results compared to Darwinian Particle Swarm Optimization (DPSO) and Electro magnetism optimization (EMO) for the parameter: convergence speed, PSNR and SSIM values.
Correction to: An adaptive Lévy flight firefly algorithm for multilevel image thresholding based on Rényi entropy
