Feature detection and description for image matching: from hand-crafted design to deep learning

• Published in: Geo-spatial information science Vol. 24; no. 1; pp. 58 - 74
• Main Authors: Chen, Lin, Rottensteiner, Franz, Heipke, Christian
• Format: Journal Article
• Language: English
• Published: Wuhan Taylor & Francis 02.01.2021; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: affine shape estimation; Algorithms; Conjugate points; Deep learning; descriptor learning; feature orientation; Image matching; image orientation; Machine learning; Matching; Training
• ISSN: 1009-5020; 1993-5153
• DOI: 10.1080/10095020.2020.1843376

In feature based image matching, distinctive features in images are detected and represented by feature descriptors. Matching is then carried out by assessing the similarity of the descriptors of potentially conjugate points. In this paper, we first shortly discuss the general framework. Then, we review feature detection as well as the determination of affine shape and orientation of local features, before analyzing feature description in more detail. In the feature description review, the general framework of local feature description is presented first. Then, the review discusses the evolution from hand-crafted feature descriptors, e.g. SIFT (Scale Invariant Feature Transform), to machine learning and deep learning based descriptors. The machine learning models, the training loss and the respective training data of learning-based algorithms are looked at in more detail; subsequently the various advantages and challenges of the different approaches are discussed. Finally, we present and assess some current research directions before concluding the paper.