Before deep learning (ca 2013), the SOTA was using descriptors: KAZE, SIFT, SURF, ORB etc use keypoints & descriptors based matching.[1] . Other approaches reli

Before deep learning (ca 2013), the SOTA was using descriptors: KAZE, SIFT, SURF, ORB etc use keypoints & descriptors based matching.[1] . Other approaches relied on shape (edge), color matching using color and shape histograms comparisons e.g. HOG [2]. Plenty of approaches existed prior to deep learning, it's just that DL just blew those out of the water with its performance. p.s. BTW I would bucket these approaches as ML too. ML >> DL [1] https://docs.opencv.org/master/db/d27/tutorial_py_table_of_contents_feature2d.html https://docs.opencv.org/master/db/d27/tutorial_py_table_of_c... [2] https://docs.opencv.org/master/d5/d33/structcv_1_1HOGDescriptor.html https://docs.opencv.org/master/d5/d33/structcv_1_1HOGDescrip...