Features extraction for image identification using computer vision
It addresses the problem of improving image identification for computer vision researchers, but is incremental as it reviews existing methods without introducing new ones.
This study examined various feature extraction techniques for image identification in computer vision, focusing on Vision Transformers (ViTs) and other methods, and summarized their architectures and performance compared to conventional CNNs.
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.