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A Genetic Algorithm Based Feature Selection Approach for Microstructural Image Classification

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Abstract

Microstructure determines the most important factors that influence all aspects of the physical properties of the metal. Machine learning based systems allow us to look at the images to find the features of microstructure images which will be useful for classifying such images. These classification outcomes are the fundamental data for many material scientists. However, handcrafted feature vectors extracted by some means may involve a significant amount of irrelevant and redundant features, which may lead to misclassification of the microstructural images. In this paper, at first, a modified version of texture-based feature descriptor, Local Tetra Pattern (LTrP), which is named as Uniform variant of LTrP (ULTrP) is used to extract the features from the microstructural images. Then a feature selection algorithm based on Genetic Algorithm (GA), named as Diversification of Population (DP) in GA (DPGA), is proposed which is applied on ULTrP to remove the possible redundant features present therein. To assess fitness of the candidate solutions, instead of applying a learning algorithm, which is a common trend, the proposed DPGA uses an ensemble of three filter ranking methods. Impressive outcomes obtained by evaluating the proposed classification framework on a standard 7-class microstructural image dataset confirm its superiority over some state-of-the-art methods.

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Availability of Data and Material

The dataset of the micrograph is considered here is publicly available at https://www.doitpoms.ac.uk

Code Availability

The source code for our method is our customized code written in Python 3.6. The source code for the feature selection algorithm is provided here: https://github.com/ahk4815/DPGA

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The authors declare that no funding support from any organization or agency.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India

    Ali Hussain Khan & Ram Sarkar

  2. Department of Computer Science and Engineering, Government College of Engineering & Textile Technology Berhampore, Berhampore, 742101, India

    Shib Sankar Sarkar

  3. Department of Computer Science and Engineering, University of Kalyani, Kalyani, 741235, India

    Kalyani Mali

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  1. Ali Hussain Khan
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  2. Shib Sankar Sarkar
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Correspondence to Shib Sankar Sarkar.

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Khan, A., Sarkar, S., Mali, K. et al. A Genetic Algorithm Based Feature Selection Approach for Microstructural Image Classification. Exp Tech 46, 335–347 (2022). https://doi.org/10.1007/s40799-021-00470-4

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