Affiliations: [a] Department of ECE, GOJAN School of Business and Technology, Chennai | [b] Mathematics Department, Saveetha School of Engineering, SIMATS, Thandalam Campus, Tamil Nadu, India
| [c] Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Saudi Arabia
| [d] Department of ECE, RV College of Engineering, Bangalore
Correspondence:
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Corresponding author. Dr. S. Lakshmi Narayanan, Professor, Department of ECE, GOJAN School of Business and Technology, Chennai. E-mail: s.lakshminarayanan@gojaneducation.com.
Abstract: A Gaussian Curvature-based Local Tetra Descriptor (GCLTrP) is proposed in this paper to incorporate geometric discriminative feature extraction using a hybrid combination of Gaussian Curvature (GC) and Local Terta Pattern (LTrP). The texture of an image is locally discriminant, capturing the equivalent binary response from the gaussian curvature. The extracted feature value is fed into the Enhanced Grey Wolf Optimization (EGWO), a lightweight metaheuristic searching algorithm that selects the best optimal textural features. The proposed GCLTrP with EGWO method’s effective performance is validated using the benchmarks dataset, and the results are tested using the performance evaluation metric. In comparison to other cutting-edge methods, the proposed method achieves the highest overall classification accuracy of 100% on the Brodatz and RS datasets. In terms of computational redundancy and noise reduction, the proposed technique outperforms the other existing techniques.
