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We propose a method for localizing and recognizing brand logos in natural images. The task is extremely challenging, due to the various changes in the appearance of the logos. We construct class templates by matching features between examples of the same class to build homographies. An interconnections graph is developed for each class and the representative points are added to the class model. Finally, each class is depicted by the reunion of the suitable keypoints and descriptors, thus leading to a high precision of the proposed logo recognition system. Results show that we outperform the state of the art systems on the challenging Flickr-32 database.

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We propose a method for localization and classification of brand logos in natural images. The system has to overcome multiple challenges such as perspective deformations, warping, variations of the shape and colors, occlusions, background variations. To deal with perspective variation, we rely on homography matching between the SIFT keypoints of logo instances of the same class. To address the changes in color, we construct a weighted graph of logo interconnections that is further analyzed to extract potentially multiple instances of the class. The main instance is build by grouping the keypoints of the graph connected logos onto the central image. The secondary instance is needed for color inverted logos and is obtained by inverting the orientation of the main instance. The constructed logo recognition system is tested on two databases (FlickrLogos-32 and BelgaLogos), outperforming state of the art with more than 10% accuracy.to be published in Machine Vision and Application. For final version please check on Springer

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