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Global biotic cross-pollination algorithm enhanced with evolutionary strategies for color image segmentation

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Abstract

Object segmentation is a prominent and challenging issue pertaining to image analysis and computer vision applications. This segmentation enables a higher number of applications like image retrieval, object recognition and object reconstruction. Considering this importance of object segmentation, the ultimate aim of the proposed research work in this paper is to focus on color image segmentation that is visually triggered using the GBCPA, popularly known as global biotic cross-pollination algorithm dependent on evolutionary strategy models. Evolutionary techniques imitate the procedure of genetic evolution and make utilization of the operators of recombination, mutation and selection to create the new-generation individuals. The new flower pollination optimization algorithm (FPOA) proposed in this paper aims to improve the objective characteristics of the basic FPOA approach. The significant contribution here mainly focused on preventing the premature convergence for dealing with the complicated constraints of the color image segmentation problem. The performance of GBCPA is tested over a Gould segmentation data set comprising of 715 images, both urban and rural images. The data set is tested under various evaluation techniques, and the proposed algorithm results in better accuracy than the other methods considered in comparison with the existing literature.

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Abbreviations

GBCPA:

Global biotic cross-pollination algorithm

ES:

Evolutionary strategy

FPOA:

Flower pollination optimization algorithm

GDS:

Gould segmentation data set

CIS:

Color image segmentation

FCM:

Fuzzy C-means

IAFHA:

Improved ant colony—fuzzy C-means hybrid algorithm

AJNDH:

Agglomerated just noticeable difference histogram

RFHA:

Region splitting and merging fuzzy C-implies hybrid algorithm

MATLAB:

Matrix Laboratory

AS:

Ant system

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Authors and Affiliations

  1. Department of EEE, Anna University: Regional Campus, Coimbatore, 641 046, India

    S. N. Deepa

  2. Department of CSE, Hindusthan College of Engineering and Technology, Othakalmandapam P.O, Coimbatore, 641 032, India

    D. Rasi

Authors
  1. S. N. Deepa
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  2. D. Rasi
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Contributions

Author Dr. D. Rasi, the corresponding author, has worked on the methodology section and development of the proposed algorithm by hybridizing the techniques. Her contributions also include carrying out simulation process of the proposed algorithm for the considered data sets. Author Dr. S.N. Deepa contributed to the selection of data sets and implementing the proposed algorithm on this data set. She has worked on the MATLAB environment in generating the simulation results and validating the attained results with the existing ones from the literature to prove its effectiveness.

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Correspondence to S. N. Deepa.

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The authors declare that there is no conflict of interest.

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Communicated by P. Pandian.

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Deepa, S.N., Rasi, D. Global biotic cross-pollination algorithm enhanced with evolutionary strategies for color image segmentation. Soft Comput 23, 2545–2559 (2019). https://doi.org/10.1007/s00500-018-03720-7

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