Enhanced image feature coverage: Key-point selection using genetic algorithms

TL;DR

This paper introduces a genetic algorithm-based method for selecting image features that maximizes coverage, improving homography estimation accuracy while reducing computational load.

Contribution

It proposes a novel feature selection approach using genetic algorithms to optimize feature coverage for vision tasks.

Findings

Enhanced feature coverage improves homography accuracy.

Selected features reduce processing time for descriptor calculation.

The method outperforms traditional feature selection in coverage and efficiency.

Abstract

Coverage of image features play an important role in many vision algorithms since their distribution affect the estimated homography. This paper presents a Genetic Algorithm (GA) in order to select the optimal set of features yielding maximum coverage of the image which is measured by a robust method based on spatial statistics. It is shown with statistical tests on two datasets that the metric yields better coverage and this is also confirmed by an accuracy test on the computed homography for the original set and the newly selected set of features. Results have demonstrated that the new set has similar performance in terms of the accuracy of the computed homography with the original one with an extra benefit of using fewer number of features ultimately reducing the time required for descriptor calculation and matching.