Performance Analysis of Keypoint Detectors and Binary Descriptors Under Varying Degrees of Photometric and Geometric Transformations

TL;DR

This study systematically evaluates eight binary descriptors and eight interest point detectors to understand their performance under various photometric and geometric transformations, providing insights for selecting robust feature matching methods.

Contribution

The paper offers a comprehensive analysis of detector-descriptor combinations, highlighting their strengths and weaknesses under different image transformations, which was lacking in prior comparative studies.

Findings

FAST, AGAST, ORB are faster and detect more keypoints.

AKAZE and KAZE perform better under photometric changes.

BRISK, FREAK, AKAZE show resilience to geometric transformations.

Abstract

Detecting image correspondences by feature matching forms the basis of numerous computer vision applications. Several detectors and descriptors have been presented in the past, addressing the efficient generation of features from interest points (keypoints) in an image. In this paper, we investigate eight binary descriptors (AKAZE, BoostDesc, BRIEF, BRISK, FREAK, LATCH, LUCID, and ORB) and eight interest point detector (AGAST, AKAZE, BRISK, FAST, HarrisLapalce, KAZE, ORB, and StarDetector). We have decoupled the detection and description phase to analyze the interest point detectors and then evaluate the performance of the pairwise combination of different detectors and descriptors. We conducted experiments on a standard dataset and analyzed the comparative performance of each method under different image transformations. We observed that: (1) the FAST, AGAST, ORB detectors were faster and detected more keypoints, (2) the AKAZE and KAZE detectors performed better under photometric changes while ORB was more robust against geometric changes, (3) in general, descriptors performed better when paired with the KAZE and AKAZE detectors, (4) the BRIEF, LUCID, ORB descriptors were relatively faster, and (5) none of the descriptors did particularly well under geometric transformations, only BRISK, FREAK, and AKAZE showed reasonable resiliency.