Correlation-Based Burstiness for Logo Retrieval
Résumé
Detecting logos in photos is challenging. A reason is that logos locally resemble patterns frequently seen in random images. We propose to learn a statistical model for the distribution of incorrect detections output by an image matching algorithm. It results in a novel scoring criterion in which the weight of correlated keypoint matches is reduced, penalizing irrelevant logo detections. In experiments on two very diff erent logo retrieval benchmarks, our approach largely improves over the standard matching criterion as well as other state-of-the-art approaches.
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| Origine | Fichiers produits par l'(les) auteur(s) |
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| Format | Figure, Image |
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