Handcrafted vs. non-handcrafted features for computer vision classification

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YOLO-based Object Detection Models: A Review and its Applications (2024, unknown): “…certain limitations inherent in these conventional methods is exemplified by the following considerations: Traditional methods often involve handcrafted features and separate steps for object detection, which might limit their ability to learn intricate patterns and variations in objects [91].”
Fundus Image Classification Using VGG-19 Architecture with PCA and SVD (2018, background): “[37] designed a computer vision system based on non-handcrafted and handcrafted features.”
COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios (2020, unknown): “In this sense, we can take advantage from the complementarity that may exist between both strategies used to obtain the descriptors, once they not necessarily make the same errors on the accomplishment of a given classiﬁcation task, as shown in [44] and [45].”
Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data (2017, unknown): “Previous studies reveal the transferability of different layers in deep CNNs trained with ImageNet dataset, and the transferring results show better performance than other standard approaches on different datasets, such as medical image datasets [26,27], X-ray security screening images [28] and the ”