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All Journal Jurnal Pilar Nusa Mandiri Communications in Science and Technology METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Journal of Intelligent Computing and Health Informatics (JICHI) Journal of Novel Engineering Science and Technology Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Saputra, Irwansyah
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Mechanical Engineering Medicine & Pharmacology Public Health

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Journal : Journal of Intelligent Computing and Health Informatics (JICHI)

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Evaluation of a Semantic Representation-Based Retrieval Model on a Text Dataset Generated from Image Transformation Firmansyah, Muhammad; Marutho, Dhendra; Ilham, Ahmad; Saputra, Irwansyah
Journal of Intelligent Computing & Health Informatics Vol 6, No 2 (2025): September
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v6i2.19240

Abstract

The increasing demand for efficient multimodal information retrieval has driven significant research into bridging visual and textual data. While sophisticated models like CLIP offer state-of-the-art semantic alignment, their substantial computational requirements present challenges for deployment in resource-constrained environments. This study introduces a lightweight retrieval framework that leverages the BLIP image captioning model to transform image data into rich textual descriptions, effectively reframing cross-modal retrieval as a text-to-text task. We systematically evaluated three retrieval models BM25, SBERT, and T5 on caption-transformed MSCOCO and Flickr30K datasets, utilizing both classical metrics (Recall@5, mAP) and semantic-aware metrics (SAR@5, Semantic mAP). Experimental results demonstrate that T5 achieves superior semantic performance (SAR@5 = 0.561, Semantic mAP = 0.524), surpassing SBERT (SAR@5 = 0.524) and outperforming the lexical BM25 baseline (SAR@5 = 0.312). Notably, the proposed BLIP+T5 pipeline attains 88% of CLIP’s semantic accuracy while reducing inference latency by approximately 60% and decreasing GPU memory consumption by over 60%. These findings underscore the potential of caption-based retrieval frameworks as scalable, cost-effective alternatives to computationally intensive multimodal systems, especially in latency-sensitive and resource-limited scenarios. Future work will explore fine-tuning strategies, domain-adapted semantic metrics, and robustness under real-world conditions to further advance retrieval effectiveness.
Co-Authors Andra, Muhammad Bagus Asrul Dhendra Marutho Fachri Amsury Ida Ayu Putu Sri Widnyani Ika Kurniawati Ilham, Ahmad Irman Hermadi Marcelly, Frizca Fellicita Muhammad Firmansyah, Muhammad nanang ruhyana Nur Arifin Akbar Parulian, Onesinus Saut R. Suharyadi Rizki Fahdia, Muhammad RR. Ella Evrita Hestiandari Sunendar, Nendi Sunendar Sutedja, Indrajani Yandra Arkeman