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Multi-level image thresholding using Otsu and chaotic bat algorithm

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Abstract

Multi-level thresholding is a helpful tool for several image segmentation applications. Evaluating the optimal thresholds can be applied using a widely adopted extensive scheme called Otsu’s thresholding. In the current work, bi-level and multi-level threshold procedures are proposed based on their histogram using Otsu’s between-class variance and a novel chaotic bat algorithm (CBA). Maximization of between-class variance function in Otsu technique is used as the objective function to obtain the optimum thresholds for the considered grayscale images. The proposed procedure is applied on a standard test images set of sizes (512 × 512) and (481 × 321). Further, the proposed approach performance is compared with heuristic procedures, such as particle swarm optimization, bacterial foraging optimization, firefly algorithm and bat algorithm. The evaluation assessment between the proposed and existing algorithms is conceded using evaluation metrics, namely root-mean-square error, peak signal to noise ratio, structural similarity index, objective function, and CPU time/iteration number of the optimization-based search. The results established that the proposed CBA provided better outcome for maximum number cases compared to its alternatives. Therefore, it can be applied in complex image processing such as automatic target recognition.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, 531162, India

    Suresh Chandra Satapathy

  2. Department of Electronics and Instrumentation Engineering, St. Joseph’s College of Engineering, Chennai, 600119, India

    N. Sri Madhava Raja & V. Rajinikanth

  3. Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt

    Amira S. Ashour

  4. Department of Information Technology, Techno India College of Technology, Kolkata, India

    Nilanjan Dey

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  1. Suresh Chandra Satapathy
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Correspondence to Amira S. Ashour.

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Satapathy, S.C., Sri Madhava Raja, N., Rajinikanth, V. et al. Multi-level image thresholding using Otsu and chaotic bat algorithm. Neural Comput & Applic 29, 1285–1307 (2018). https://doi.org/10.1007/s00521-016-2645-5

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