Abstract

Introduction: Image searching is a continual challenge even with the many image retrieval models that have sprung up. Sketch-Based Image Retrieval (SBIR) models attempt to solve this challenge by searching using sketching. The existing SBIR algorithms have limited performance because of ambiguities and variations in hand-drawn sketches. Aims: The aim of this work was to review and identify the strengths and weaknesses of the existing SBIR models. Materials and Methods: Articles were selected from Google Scholar assessing strictly sketch construction models. Search terms include sketch construction, sketch-based image retrieval, hypermedia, multimedia, design strategies, and algorithms. Results: The search returned 455 articles of which only 134 studies met the inclusion criteria. 30 papers were on Convolutional Neural Network (CNN) and hybrids. 6 on Contour and Stroke Segments. 4 on Generative Adversarial Network while 3 papers were on Deep Hashing. 6 papers reported use of 3D-CNN-based methods while 85 papers used other methods like sparse coding and bag of regions. Accuracy, recall and precision ranged from 59.47% to 99.4%, 20.10% to 47.70% and 33.40% to 51.00% respectively. Conclusion: There are some promising SBIR models but lots of effort is required if computational SBIRs are to be adopted. Most studies did not include any performance metric which makes it difficult to assess the performances of the algorithms proposed. Researchers are advised to always report the performance algorithms. The future plan is to develop a robust SBIR algorithm which will accommodate handwriting ambiguity variations

Keywords: Sketch construction, Sketch-based image retrieval, Hypermedia, Information retrieval, and Image retrieval