Correlation-Based Burstiness for Logo Retrieval

Jérôme Revaud 1 Matthijs Douze 1, 2 Cordelia Schmid 1
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : 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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Jérôme Revaud, Matthijs Douze, Cordelia Schmid. Correlation-Based Burstiness for Logo Retrieval. MM 2012 - ACM International Conference on Multimedia, Oct 2012, Nara, Japan. pp.965-968, ⟨10.1145/2393347.2396358⟩. ⟨hal-00728502⟩

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