Classification of image registration problems using support vector machines

Abstract

This paper introduces a system that automatically classifies image pairs based on the type of registration required to align them. The system uses support vector machines to classify between panoramas, high-dynamic-range images, focal stacks, super-resolution, and unrelated image pairs. A feature vector was developed to describe the images, and 1100 pairs were used to train and test the system with 5-fold cross validation. The system is able to classify the desired registration application using a 1: Many classifier with an accuracy of 91.18%. Similarly 1:1 classifiers were developed for each class with classification rates as follows: Panorama image pairs are classified at 93.15%, high-dynamic-range pairs at 97.56%, focal stack pairs at 95.68%, super-resolution pairs at 99.25%, and finally unrelated image pairs at 95.79%. An investigation into feature importance outlines the utility of each feature individually. In addition, the invariance of the classification system towards the size of the image used to calculate the feature vector was explored. The classification of our system remains level at ∼91% until the image size is scaled to 10% (150 × 100 pixels), suggesting that our feature vector is image size invariant within this range.