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Maulana Taufiqurrohman
Faculty of Computer Science, Informatics & Business Institute Darmajaya, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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A Comparative Study of Deep Learning Architectures for Content-Based Image Retrieval on a Stone Texture Dataset Maulana Taufiqurrohman; Suhendro Irianto
Scientific Journal of Informatics Vol. 13 No. 3: August 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i3.51297

Abstract

Purpose: This study addresses a persistent gap in geological content-based image retrieval (CBIR): the reliance of existing sequence-based and hybrid deep learning models on static, two-dimensional rock texture representations that they were not originally designed to handle. Methods: Four architectures were evaluated on a unified 9,853-image, ten-category Stone-2D dataset, partitioned into training and validation subsets at an 8:2 ratio (7,882/1,971 images), with FAISS employed for similarity search over 128-dimensional embeddings. Findings: On an identical held-out test set, all four architectures achieved comparable performance (82.4%–85.2% accuracy; macro F1 0.826–0.860), with a standard 2D CNN achieving the highest accuracy (85.2%) and the custom pseudo-3D MineralNet architecture achieving the highest macro F1 (0.860). No architecture dominated across both metrics. Novelty: These results indicate that, once preprocessing is aligned with the native dimensionality of the image data, architectural complexity provides limited additional benefit for static geological texture classification — challenging the common assumption that specialized or sequential architectures are necessary for this task. A working CBIR retrieval pipeline was validated with representative query examples, demonstrating semantically consistent nearest-neighbour retrieval across distinct rock textures.
Co-Authors Suhendro Irianto