Exploring self-supervised feature extraction techniques in the K-views algorithm for texture analysis

Burak Kure; Min Wang; Coskun Cetinkaya; Chih-Cheng Hung; Burak Kure; Min Wang; Coskun Cetinkaya; Chih-Cheng Hung

doi:10.3934/aci.2025008

Applied Computing and Intelligence

2025, Volume 5, Issue 1: 112-126. doi: 10.3934/aci.2025008

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Research article

Exploring self-supervised feature extraction techniques in the K-views algorithm for texture analysis

1.
Laboratory of Machine Vision and Security Research, College of Computing and Software Engineering, Kennesaw State University, Marietta, GA, USA
2.
Department of Mathematics, Kennesaw State University, Kennesaw, GA, USA

Academic Editor: Pasi Franti

Received: 10 December 2024 Revised: 04 April 2025 Accepted: 09 April 2025 Published: 16 April 2025

This paper explored self-supervised feature extraction in the K-views algorithm for texture analysis, which can reduce the reliance on labeled data. The study compared the supervised, and self-supervised methods, focusing on pair-wise feature comparison to assess the similarity and dissimilarity between texture classes. Among the techniques evaluated, Siamese networks achieved the highest performance, while self-supervised methods like rotation prediction demonstrated competitive results and scalability. Experimental results on KTH-TIPS, Kylberg, and UIUC datasets show the potential of self-supervised approaches in improving texture classification by leveraging feature representations without the need for manually labeled data for the K-views algorithm.
- K-views algorithm,
- texture analysis,
- image processing,
- machine learning,
- deep learning,
- Siamese networks,
- self-supervised learning
Citation: Burak Kure, Min Wang, Coskun Cetinkaya, Chih-Cheng Hung. Exploring self-supervised feature extraction techniques in the K-views algorithm for texture analysis[J]. Applied Computing and Intelligence, 2025, 5(1): 112-126. doi: 10.3934/aci.2025008

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Abstract

This paper explored self-supervised feature extraction in the K-views algorithm for texture analysis, which can reduce the reliance on labeled data. The study compared the supervised, and self-supervised methods, focusing on pair-wise feature comparison to assess the similarity and dissimilarity between texture classes. Among the techniques evaluated, Siamese networks achieved the highest performance, while self-supervised methods like rotation prediction demonstrated competitive results and scalability. Experimental results on KTH-TIPS, Kylberg, and UIUC datasets show the potential of self-supervised approaches in improving texture classification by leveraging feature representations without the need for manually labeled data for the K-views algorithm.

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