1 Department of Computing and Information System, Kenyatta University, Kenya.
2 Department of Mathematics, Institute for Basic Science, Technology and Innovation, Pan-African University, Kenya.
3 Department of Software Engineering, College of Software, Nankai University, China.
World Journal of Advanced Research and Reviews, 2025, 27(01), 1341-1351
Article DOI: 10.30574/wjarr.2025.27.1.2647
Received on 01 June 2025; revised on 12 July 2025; accepted on 14 July 2025
This study examines various feature extraction techniques in computer vision, the primary focus of which is on Vision Transformers (ViTs) and other approaches such as Generative Adversarial Networks (GANs), deep feature models, traditional approaches (SIFT, SURF, ORB), and non-contrastive and contrastive feature models. Emphasizing ViTs, the report summarizes their architecture, including patch embedding, positional encoding, and multi-head self-attention mechanisms with which they overperform conventional convolutional neural networks (CNNs). Experimental results determine the merits and limitations of both methods and their utilitarian applications in advancing computer vision.
Feature Extraction; Positional Embeddings; Self-Attention; Vision Transformers (ViTs)
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Venant Niyonkuru, Sylla Sekou and Jimmy Jackson Sinzinkayo. Features extraction for image identification using computer vision. World Journal of Advanced Research and Reviews, 2025, 27(01), 1341-1351. Article DOI: https://doi.org/10.30574/wjarr.2025.27.1.2647.
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