title: Context-dependent logo matching and recognition.
abstract: We contribute, through this paper, to the design of a novel variational framework able to match and recognize multiple instances of multiple reference logos in image archives. Reference logos and test images are seen as constellations of local features (interest points, regions, etc.) and matched by minimizing an energy function mixing: 1) a fidelity term that measures the quality of feature matching, 2) a neighborhood criterion that captures feature co-occurrence/geometry, and 3) a regularization term that controls the smoothness of the matching solution. We also introduce a detection/recognition procedure and study its theoretical consistency. Finally, we show the validity of our method through extensive experiments on the challenging MICC-Logos dataset. Our method overtakes, by 20%, baseline as well as state-of-the-art matching/recognition procedures.
authors.affiliation: CNRS LTCI, Télécom ParisTech, Paris 75013, France. hichem.sahbi@telecom-paristech.fr
authors.forename: Hichem
authors.initials: H
authors.lastname: Sahbi
authors.forename: Lamberto
authors.initials: L
authors.lastname: Ballan
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Serra
authors.forename: Alberto
authors.initials: A
authors.lastname: Del Bimbo
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004282:Documentation / Q000379:methods*; D004616:Emblems and Insignia / Q000145:classification*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique