MID: A Novel Contrast Metric for the MSER Detector

This paper presents a novel contrast measure for MSER region selection, termed Mean Intensity Difference (MID). The proposed metric is computed between the pixels of an MSER region and its surrounding pixel set. In this work we consider the complementary pixels within the bounding box of a region and alternatively the pixels of the first contour layer as surroundings. To evaluate the proposed contrast metric, a location retrieval task is performed. To this end, SURF descriptors are computed and the Bag-of-Words representation is used as global signature for each image. For the evaluation we use the Devon Island dataset, which is said to have one of the most Mars-like environments on Earth and which comes with GPS ground-truth data. We further integrate the contrast-based methods with the approach of Grid Adaptation. The experimental results show that our contrast metric outperforms state-of-the-art metrics, such as Perceptual Divergence, and yields better performance compared to random region selection. In this work we also evaluate the computational complexity of the methods.