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A Systematic Literature Review of Deep Learning Approaches for Sketch-Based Image Retrieval: Datasets, Metrics, and Future Directions
Universiti Teknologi Malaysia (UTM), Malaysia.
Universiti Teknologi Malaysia (UTM), Malaysia.
Universiti Teknologi Malaysia (UTM), Malaysia.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0003-4071-4596
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2024 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 12, p. 14847-14869Article, review/survey (Refereed) Published
Abstract [en]

Sketch-based image retrieval (SBIR) utilizes sketches to search for images containing similar objects or scenes. Due to the proliferation of touch-screen devices, sketching has become more accessible and therefore has received increasing attention. Deep learning has emerged as a potential tool for SBIR, allowing models to automatically extract image features and learn from large amounts of data. To the best of our knowledge, there is currently no systematic literature review (SLR) of SBIR with deep learning. Therefore, the aim of this review is to incorporate related works into a systematic study, highlighting the main contributions of individual researchers over the years, with a focus on past, present and future trends. To achieve the purpose of this study, 90 studies from 2016 to June 2023 in 4 databases were collected and analyzed using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) framework. The specific models, datasets, evaluation metrics, and applications of deep learning in SBIR are discussed in detail. This study found that Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN) are the most widely used deep learning methods for SBIR. A commonly used dataset is Sketchy, especially in the latest Zero-shot sketch-based image retrieval (ZS-SBIR) task. The results show that Mean Average Precision (mAP) is the most commonly used metric for quantitative evaluation of SBIR. Finally, we provide some future directions and guidance for researchers based on the results of this review. © 2013 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024. Vol. 12, p. 14847-14869
Keywords [en]
deep learning, PRISMA, SBIR, Sketch-based image retrieval, SLR, Generative adversarial networks, Image processing, Image retrieval, Neural networks, Touch screens, Features extraction, Meta-analysis, Preferred reporting item for systematic review and meta-analyze, Sketch-based image retrievals, Systematic, Systematic literature review, Systematic Review, Feature extraction
National Category
Computer graphics and computer vision
Identifiers
URN: urn:nbn:se:bth-25972DOI: 10.1109/ACCESS.2024.3357939ISI: 001159093400001Scopus ID: 2-s2.0-85184000059OAI: oai:DiVA.org:bth-25972DiVA, id: diva2:1838437
Available from: 2024-02-16 Created: 2024-02-16 Last updated: 2025-02-07Bibliographically approved

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Kebande, Victor R.

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