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

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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.

Keywords

K-views algorithm / texture analysis / image processing / machine learning / deep learning / Siamese networks / self-supervised learning

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