TY  - JOUR
AU  - Pedro Martins
AU  - Paulo Carvalho
AU  - Carlo Gatta
PY  - 2016//
TI  - On the completeness of feature-driven maximally stable extremal regions
T2  - PRL
JO  - Pattern Recognition Letters
SP  - 9
EP  - 16
VL  - 74
PB  - Elsevier B.V.
KW  - Local features
KW  - Completeness
KW  - Maximally Stable Extremal Regions
N2  - 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.
SN  - 0167-8655
L1  - http://refbase.cvc.uab.es/files/MCG2016.pdf
UR  - http://dx.doi.org/doi:10.1016/j.patrec.2016.01.003
N1  - LAMP;MILAB;
ID  - Pedro Martins2016
ER  -