Rapid Online Analysis of Local Feature Detectors and Their Complementarity

TL;DR

This paper introduces a rapid, online metric based on spatial distribution of local image features to assess and improve the performance and complementarity of feature detectors in vision systems.

Contribution

It proposes a novel, fast performance indicator for local feature detectors that correlates with human assessments and guides detector combination strategies.

Findings

The metric aligns with human visual judgments.

It effectively measures detector complementarity.

Statistically significant performance differences are identified.

Abstract

A vision system that can assess its own performance and take appropriate actions online to maximize its effectiveness would be a step towards achieving the long-cherished goal of imitating humans. This paper proposes a method for performing an online performance analysis of local feature detectors, the primary stage of many practical vision systems. It advocates the spatial distribution of local image features as a good performance indicator and presents a metric that can be calculated rapidly, concurs with human visual assessments and is complementary to existing offline measures such as repeatability. The metric is shown to provide a measure of complementarity for combinations of detectors, correctly reflecting the underlying principles of individual detectors. Qualitative results on well-established datasets for several state-of-the-art detectors are presented based on the proposed measure. Using a hypothesis testing approach and a newly-acquired, larger image database, statistically-significant performance differences are identified. Different detector pairs and triplets are examined quantitatively and the results provide a useful guideline for combining detectors in applications that require a reasonable spatial distribution of image features. A principled framework for combining feature detectors in these applications is also presented. Timing results reveal the potential of the metric for online applications.