Evaluation of Distance Measures for Feature based Image Registration using AlexNet

TL;DR

This paper investigates the effectiveness of different distance measures for feature-based image registration using deep features extracted from AlexNet, comparing their performance in matching and registration accuracy.

Contribution

It evaluates various distance metrics for deep CNN features in image registration, providing insights into optimal choices for improved matching performance.

Findings

Certain distance measures outperform others in registration accuracy.

Deep features from AlexNet improve registration over traditional methods.

Evaluation on benchmark datasets validates the proposed approach.

Abstract

Image registration is a classic problem of computer vision with several applications across areas like defence, remote sensing, medicine etc. Feature based image registration methods traditionally used hand-crafted feature extraction algorithms, which detect key points in an image and describe them using a region around the point. Such features are matched using a threshold either on distances or ratio of distances computed between the feature descriptors. Evolution of deep learning, in particular convolution neural networks, has enabled researchers to address several problems of vision such as recognition, tracking, localization etc. Outputs of convolution layers or fully connected layers of CNN which has been trained for applications like visual recognition are proved to be effective when used as features in other applications such as retrieval. In this work, a deep CNN, AlexNet, is used in the place of handcrafted features for feature extraction in the first stage of image registration. However, there is a need to identify a suitable distance measure and a matching method for effective results. Several distance metrics have been evaluated in the framework of nearest neighbour and nearest neighbour ratio matching methods using benchmark dataset. Evaluation is done by comparing matching and registration performance using metrics computed from ground truth. Keywords: Distance measures; deep learning; feature detection; feature descriptor; image matching