A Review of Bio-inspired Algorithms as Image Processing Techniques

Khalid, Norlin; Ariff, Norharyati Md; Yahya, Saadiah; Noor, Noorhayati Mohamed

doi:10.1007/978-3-642-22170-5_57

Cited by 12 publications

(1 citation statement)

References 54 publications

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“…In the last few years, nature-inspired algorithms and metaheuristics have been used to address the image registration problem, becoming a solid alternative to direct optimization methods. Even though image registration using the nature-inspired algorithms represents a niche in the research, a variety of evolutionary algorithm techniques has been proposed, starting from the most used, Genetic Algorithm, and moving to the newest, Coral Reefs Optimization [4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Firefly-Based Approaches of Image Recognition

Cocianu¹,

Stan²,

Avramescu³

2020

Symmetry

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The main aim of the reported work is to solve the registration problem for recognition purposes. We introduce two new evolutionary algorithms (EA) consisting of population-based search methods, followed by or combined with a local search scheme. We used a variant of the Firefly algorithm to conduct the population-based search, while the local exploration was implemented by the Two-Membered Evolutionary Strategy (2M-ES). Both algorithms use fitness function based on mutual information (MI) to direct the exploration toward an appropriate candidate solution. A good similarity measure is the one that enables us to predict well, and with the symmetric MI we tie similarity between two objects A and B directly to how well A predicts B, and vice versa. Since the search landscape of normalized mutual information proved more amenable for evolutionary computation algorithms than simple MI, we use normalized mutual information (NMI) defined as symmetric uncertainty. The proposed algorithms are tested against the well-known Principal Axes Transformation technique (PAT), a standard evolutionary strategy and a version of the Firefly algorithm developed to align images. The accuracy and the efficiency of the proposed algorithms are experimentally confirmed by our tests, both methods being excellently fitted to registering images.

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