On the completeness of feature-driven maximally stable extremal regions

• An effcient method to capture the most informative image parts.
• Different instances of feature-driven MSER are presented.
• Feature-driven MSER are assessed in terms of completeness in a large-scale test.
• Feature-driven MSER are assessed in terms of complementarity in a large-scale test.

By definition, local image features provide a compact representation of the image in which most of the image information is preserved. This capability offered by local features has been overlooked, despite being relevant in many application scenarios. In this paper, we analyze and discuss the performance of feature-driven Maximally Stable Extremal Regions (MSER) in terms of the coverage of informative image parts (completeness). This type of features results from an MSER extraction on saliency maps in which features related to objects boundaries or even symmetry axes are highlighted. These maps are intended to be suitable domains for MSER detection, allowing this detector to provide a better coverage of informative image parts. Our experimental results, which were based on a large-scale evaluation, show that feature-driven MSER have relatively high completeness values and provide more complete sets than a traditional MSER detection even when sets of similar cardinality are considered.