Image Matching: An Application-oriented Benchmark
This work addresses the problem of benchmarking image matching for computer vision applications, providing a standardized evaluation framework that is incremental but useful for researchers and practitioners.
The authors tackled the lack of a comprehensive benchmark for evaluating image matching methods by creating a uniform benchmark with application-oriented metrics and a large-scale dataset, resulting in a detailed performance evaluation of state-of-the-art methods and guidelines for practical system design.
Image matching approaches have been widely used in computer vision applications in which the image-level matching performance of matchers is critical. However, it has not been well investigated by previous works which place more emphases on evaluating local features. To this end, we present a uniform benchmark with novel evaluation metrics and a large-scale dataset for evaluating the overall performance of image matching methods. The proposed metrics are application-oriented as they emphasize application requirements for matchers. The dataset contains two portions for benchmarking video frame matching and unordered image matching separately, where each portion consists of real-world image sequences and each sequence has a specific attribute. Subsequently, we carry out a comprehensive performance evaluation of different state-of-the-art methods and conduct in-depth analyses regarding various aspects such as application requirements, matching types, and data diversity. Moreover, we shed light on how to choose appropriate approaches for different applications based on empirical results and analyses. Conclusions in this benchmark can be used as general guidelines to design practical matching systems and also advocate potential future research directions in this field.