Local Features

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Local features refer to a pattern or distinct structure found in an image, such as a point, edge, or small image patch. They are usually associated with an image patch that differs from its immediate surroundings by texture, color, or intensity. []https://www.mathworks.com/help/vision/ug/local-feature-detection-and-extraction.html Local Feature Detection and Extraction | MathWorks]



So, in 2004, D.Lowe, University of British Columbia, came up with a new algorithm, Scale Invariant Feature Transform (SIFT) in his paper, Distinctive Image Features from Scale-Invariant Keypoints, which extract keypoints and compute its descriptors. (This paper is easy to understand and considered to be best material available on SIFT. So this explanation is just a short summary of this paper). Introduction to SIFT (Scale-Invariant Feature Transform) | OpenCV


LF-Net has two main components. The first one is a dense, multi-scale, fully convolutional network that returns keypoint locations, scales, and orientations. It is designed to achieve fast inference time, and to be agnostic to image size. The second is a network that outputs local descriptors given patches cropped around the keypoints produced by the first network. We call them detector and descriptor. ...presented a novel deep architecture and a training strategy to learn a local feature pipeline from scratch, using collections of images without the need for human supervision ... propose a sparse-matching method with a novel deep architecture, which we name LF-Net, for Local Feature Network, that is trainable end-to-end and does not require using a hand-crafted detector to generate training data. ...local features have played a crucial role in computer vision, becoming the de facto standard for wide-baseline image matching https://papers.nips.cc/paper/7861-lf-net-learning-local-features-from-images.pdf | Y. Ono, E. Trulls, P Fua, and K.Yi]