Optimizing multilevel image segmentation with a modified new Caledonian crow learning algorithm

Abstract

Image segmentation is a fundamental component of image analysis. While numerous algorithms exist for this task, thresholding is one of the most widely used methods. Multilevel thresholding, which involves dividing an image into multiple segments, is computationally intensive due to its need to search for optimal thresholds. This paper presents a solution to this optimization problem by exploring the New Caledonian crow learning algorithm (NCCLA). Inspired by nature, the NCCLA algorithm draws from the behaviors of New Caledonian crows, which use tools from Pandanus trees to obtain food. To improve the algorithm's capacity to discover optimal thresholds while balancing the exploitation and exploration processes, this paper introduces a modification inspired by the pitch adjustment rate portion of the harmony search algorithm. The performance of this modified NCCLA algorithm was evaluated on benchmark images, and a comparative analysis was conducted against other metaheuristic-based algorithms including particle swarm optimization, harmony search, bacterial foraging, and genetic algorithms; the experimental results demonstrate the effectiveness of the proposed algorithm, which was further statistically validated using a t-test.