Survey of local invariant feature description

Local image feature description is a basic research in the field of computer vision and it is also a hotspot in the community. The paper depicts the development history of local feature description in decades. Then based on the strategy of feature pooling, it classifies feature description methods into three types: histogram based method, feature comparison based method and machine learning based method. It gives a comprehensive overview of the common methods in each class and compares them in terms of computational complexity, storage requirements and descriptor performance. Overall, histogram-based methods have the best performance in a variety of image distortions; Feature comparison based methods have the highest computational efficiency. Machine learning based methods require less storage space. At last, the challenges and future development of local feature description has been discussed.