Bandingkan metode

	Semantic Segmentation	Klasifikasi Citra
Bidang	Pembelajaran Mendalam	Pembelajaran Mendalam
Keluarga	Machine learning	Machine learning
Tahun asal	2015	2012 (deep CNN era); conceptual roots 1989 (LeCun)
Pencetus	Long, J., Shelhamer, E., & Darrell, T.	Krizhevsky, A.; Sutskever, I.; Hinton, G. E.
Tipe	Dense prediction / pixel-wise classification	Supervised classification task
Sumber perintis	Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440.	Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems (NeurIPS), 25, 1097–1105.
Alias	pixel-wise classification, scene parsing, dense labeling, semantic scene segmentation	visual classification, image recognition, CNN-based classification, visual categorization
Terkait	5	5
Ringkasan	Semantic segmentation assigns a class label to every pixel in an image, producing a dense, category-annotated map of the scene. Unlike object detection, which draws bounding boxes, it delineates the exact spatial extent of each class, making it indispensable in medical imaging, autonomous driving, satellite analysis, and any task where precise region boundaries matter.	Image classification is the task of assigning a single semantic label to an entire image from a fixed set of categories. Modern approaches rely on deep convolutional neural networks (CNNs) or Vision Transformers (ViTs) trained end-to-end on large labeled datasets such as ImageNet, achieving superhuman accuracy on many benchmarks and underpinning applications from medical imaging to autonomous vehicles.