Deep Learning based Scale Estimation for Local Image Features
The objective of this work is to develop a deep learning-based framework to estimate the characteristic scales of local image features. The characteristic scale describes the size of a feature and defines the region used to form the corresponding feature vector. Feature vectors are used as a basis for feature matching, object detection, object tracking, and other applications. Some conventional feature detection algorithms use computationally expensive and complex scale-space analysis schemes.
This work aims to replace these schemes with a deep-learning-based framework, which can be used for a range of different feature detectors. This requires two steps: creating data sets with local image features together with characteristic scales and selecting, implementing, and testing appropriate Convolutional Neural Network (CNN) architectures. The data set will be created based on existing data sets for object recognition with several thousand real images.
The features and the characteristic scales will be obtained using standard feature detectors such as SIFT, SURF, and ORB. For the CNN, the architectures from LIFT, LF-Net, and Key.Net will be considered. Using the framework, the estimated characteristic scales should be repeatable and precise.